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Syaeful, Heri, and Suharji Suharji. "Geostatistics Application On Uranium Resources Classification: Case Study of Rabau Hulu Sector, Kalan, West Kalimantan." EKSPLORIUM 39, no. 2 (January 31, 2019): 131. http://dx.doi.org/10.17146/eksplorium.2018.39.2.4960.
Повний текст джерелаАнотація:
ABSTRACT In resources estimation, geostatistics methods have been widely used with the benefit of additional attribute tools to classify resources category. However, inverse distance weighting (IDW) is the only method used previously for estimating the uranium resources in Indonesia. The IDW method provides no additional attribute that could be used to classify the resources category. The objective of research is to find the best practice on geostatistics application in uranium resource estimation adjusted with geological information and determination of acceptable geostatistics estimation attribute for resources categorization. Geostatistics analysis in Rabau Hulu Sector was started with correlation of the orebody between boreholes. The orebodies in Rabau Hulu Sectors are separated individual domain which further considered has the hard domain. The orebody-15 was selected for further geostatistics analysis due to its wide distribution and penetrated most by borehole. Stages in geostatistics analysis cover downhole composites, basic statistics analysis, outliers determination, variogram analysis, and calculation on the anisotropy ellipsoid. Geostatistics analysis shows the availability of the application for two resources estimation attributes, which are kriging efficiency and kriging variance. Based on technical judgment of the orebody continuity versus the borehole intensity, the kriging efficiency is considered compatible with geological information and could be used as parameter for determination of the resources category. ABSTRAK Pada estimasi sumber daya, metode geostatistik telah banyak digunakan dengan kelebihan adanya alat atribut tambahan untuk mengklasifikasikan kategori sumber daya. Namun demikian, pembobotan inverse distance (IDW) adalah satu-satunya metode yang sebelumnya digunakan untuk mengestimasi sumber daya uranium di Indonesia. Metode IDW tidak memberikan tambahan atribut yang dapat digunakan dalam mengklasifikasikan kategori sumber daya. Tujuan dari penelitian adalah mendapatkan praktek terbaik untuk aplikasi geostatistik pada estimasi sumber daya disesuaikan dengan informasi geologi dan penentuan atribut geostatistik yang dapat digunakan untuk kategorisasi sumber daya. Analisis geostatistik di Sektor Rabau Hulu diawali dengan korelasi tubuh bijih antara lubang bor. Tubuh-tubuh bijih di Sektor Rabau Hulu merupakan domain individual yang selanjutnya dipertimbangkan memiliki domain tegas. Tubuh bijih-15 dipilih untuk digunakan pada analisis geostatistik selanjutnya karena distribusinya yang luas dan paling banyak dipenetrasi bor. Tahapan dalam analisis geostatistik mencakup komposit downhole, analisis statistik dasar, determinasi outliers, analisis variogram, dan perhitungan ellipsoid anisotropi. Analisis geostatistik menghasilkan kemungkinan aplikasi dua atribut estimasi sumber daya, yaitu kriging efisiensi dan kriging varians. Berdasarkan penilaian teknis kemenerusan tubuh bijih terhadap intensitas lubang bor, kriging efisiensi dipertimbangkan sesuai dengan informasi geologi dan dapat digunakan sebagai parameter untuk penentuan kategori sumber daya.
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Idir, Yacine Mohamed, Olivier Orfila, Vincent Judalet, Benoit Sagot, and Patrice Chatellier. "Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics." Sensors 21, no. 14 (July 9, 2021): 4717. http://dx.doi.org/10.3390/s21144717.
Повний текст джерелаАнотація:
With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of aggregating all these data sources in order to provide precise mapping of air quality arises. In this context, we explore the spatio-temporal geostatistics methods as a solution for such a problem and evaluate three different methods: Simple Kriging (SK) in residuals, Ordinary Kriging (OK), and Kriging with External Drift (KED). On average, geostatistical models showed 26.57% improvement in the Root Mean Squared Error (RMSE) compared to the standard Inverse Distance Weighting (IDW) technique in interpolating scenarios (27.94% for KED, 26.05% for OK, and 25.71% for SK). The results showed less significant scores in extrapolating scenarios (a 12.22% decrease in the RMSE for geostatisical models compared to IDW). We conclude that univariable geostatistics is suitable for interpolating this type of data but is less appropriate for an extrapolation of non-sampled places since it does not create any information.
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AbdelRahman, Mohamed A. E., Yasser M. Zakarya, Mohamed M. Metwaly, and Georgios Koubouris. "Deciphering Soil Spatial Variability through Geostatistics and Interpolation Techniques." Sustainability 13, no. 1 (December 28, 2020): 194. http://dx.doi.org/10.3390/su13010194.
Повний текст джерелаАнотація:
Detailed knowledge of soil properties is fundamentally important for optimizing agriculture practices and management. Meanwhile, the spatial distribution of soil physicochemical properties is considered a fundamental input of any sustainable agricultural planning. In the present study, ordinary kriging, regression kriging and IDW were chosen for deciphering soil spatial variability and mapping soil properties in a reclaimed area of the Behera Governorate of Egypt where soil arose from two different types, one sandstone and the other limestone. Geostatistics were used to show the interrelationships and conditions of soil properties (available phosphorus, potassium and nitrogen, EC, pH, Sp, ESP, CEC, OC, SAR, and CaCO3). The results of mapping spatial soil variability by Geostatistics could be used for precision agriculture applications. Based on the soil test results, nutrient management recommendations should be applied regarding variable rates of fertilizers. The performance of the maps was evaluated using Mean square error (MSE). Inverse distance weight (IDW) showed higher efficiency than Kriging as a prediction method for mapping the studied soil properties in the study area. The results of the present study suggest that the application of the selected fit model worldwide in any relevant study of soil properties of different geological sources is feasible.
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Permata, Linda. "Non-linear Geostatistics Approach for An Integrated Surface Mapping in Epithermal Gold Deposit, Lampung." Journal of Science and Applicative Technology 5, no. 2 (July 1, 2021): 259. http://dx.doi.org/10.35472/jsat.v5i2.444.
Повний текст джерелаАнотація:
A conventional surface mapping is calculated by any means of linear interpolator such as nearest neighborhood point (NNP), inverse distance (IDW)/inverse distance square (IDS), polygon, contour weighing, Ordinary Kriging (OK). The latter is included in geostatistic methods and provides more advanced weighing method that differs from the rest. Although OK provides smoothing over mapping data but it does not cover categorial (non-value) data. Besides, it is not best in strongly skewed data that are common in exploration data and is limited to the expected value at some location. On the other hand, a non-linear interpolator is conducted to estimate the conditional expectation at a location, that not only to simply predict the grade or other parameter itself, but also the probability of the parameter at a location with known nearby samples. An integrated surface mapping should have many kinds of data that can be categorized into continous data (grade, thickness, elevation, etc.) and categorial data (lithology, alteration, structural data, etc.). In order to create a block that consist of all data available in a given deposit, a non-linier transformation will be conducted to estimate values at determined thresholds by Kriging methods – known as Indicator Kriging method and its variants.
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Ly, S., C. Charles, and A. Degré. "Geostatistical interpolation of daily rainfall at catchment scale: the use of several variogram models in the Ourthe and Ambleve catchments, Belgium." Hydrology and Earth System Sciences 15, no. 7 (July 18, 2011): 2259–74. http://dx.doi.org/10.5194/hess-15-2259-2011.
Повний текст джерелаАнотація:
Abstract. Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces. The first step in kriging computation is the semi-variogram modelling which usually used only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. This study leaned on 30-yr daily rainfall data of 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. We found that between the seven variogram models used, the Gaussian model was the most frequently best fit. Using seven variogram models can avoid negative daily rainfall in ordinary kriging. The negative estimates of kriging were observed for convective more than stratiform rain. The performance of the different methods varied slightly according to the density of raingages, particularly between 8 and 70 raingages but it was much different for interpolation using 4 raingages. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably the interpolation with the Thiessen polygon, commonly used in various hydrological models. Integrating elevation into Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) did not improve the interpolation accuracy for daily rainfall. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases. Care should be taken in applying UNK and KED when interpolating daily rainfall with very few neighbourhood sample points. These recommendations complement the results reported in the literature. ORK, UNK and KED using only spherical model offered a slightly better result whereas OCK using seven variogram models achieved better result.
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Logan, J., and M. A. Mueller. "Using Geospatial Techniques and GIS to Develop Maps of Freeze Probabilities and Growing Degrees." HortScience 35, no. 4 (July 2000): 558D—558c. http://dx.doi.org/10.21273/hortsci.35.4.558d.
Повний текст джерелаАнотація:
Tennessee is located in an area of diverse topography, ranging in elevation from <100 m to ≈2000 m, with numerous hills and valleys. The physiography makes it very difficult to spatially interpolate weather data related to vegetable production, such as spring and fall freeze dates and growing degree days (GDD). In addition, there is a poor distribution of cooperative weather stations, especially those with 30 years or more of data. There are climate maps available for Tennessee, but they are of such a general format as to be useless for operational applications. This project is designed to use a geographic information system (GIS) and geospatial techniques to spatially interpolate freeze (0 °C) dates and GDD for different base temperatures and make the data available as Internet-based maps. The goal is to develop reasonable climate values for vegetable growing areas <1000 m in elevation at a 100 square km resolution. The geostatistics that we are evaluating include Thiessen polygons, triangulated irregular network (TIN), inverse distance weighting (IDW), spline, kriging, and cokriging. Data from 140 locations in and around Tennessee are used in the analysis. Incomplete data from 100 other locations are used to validate the models. GDD, which have much less year-to-year variability than freeze dates, can be successfully interpolated using inverse distance weighting (IDW) or spline techniques. Even a simple method like Thiessen produces fairly accurate maps. Freeze dates, however, are better off analyzed on an annual basis because the patterns can vary significantly from year to year. The annual maps can then be superimposed to give a better estimate of average spring and fall freeze dates.
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Herlina, Herlina, and Diyono Diyono. "ANALISIS GEOSTATISTIK UNTUK PEMETAAN PERUBAHAN KUALITAS AIR TANAH KAWASAN KARST KABUPATEN GUNUNGKIDUL." Elipsoida : Jurnal Geodesi dan Geomatika 3, no. 01 (July 3, 2020): 1–12. http://dx.doi.org/10.14710/elipsoida.2020.7762.
Повний текст джерелаАнотація:
Gunungkidul Regency has a karst area of approximately 807 km2 or 53% of the total area of its territory. There is a tendency for expansion in karst mining leading to a number of potentials, including damage to the water system which is a pollution of karst water sources. Temperature, turbidity, Total Dissolve Solid (TDS), PH, hardness, manganese, iron, and chloride are parameters affecting groundwater quality. Measurement of the concentration of each parameter is performed through a long process and expensive costs. Therefore, not all measurements are performed in the entire area of Gunungkidul. Hence, it is important to interpolate the eight parameters using the geostatistical method. Geostatistical kriging method is an estimation method that reduces the error of variance estimation by a cross-correlation between primary and secondary variables. The best semivariogram for a five-year period with the smallest RMSE value is the temperature in 2018 using an gaussian model, turbidity in 2018 using a IDW model, Total Dissolve Solid (TDS) in 2017 using a gaussian model, PH in 2016 using a linear exponential, hardness in 2019 using a exponential model, manganese in 2017 using a circular model, iron in 2017 using a exponential model, and chlorides in 2015 using a RBF. Monitoring points of groundwater quality using these eight parameters have different variances so that five parameters are producing more than one RMSE value. To resolve this, besides comparing several interpolation methods, natural logarithmic transformations and the correlation of actual values with estimates were also performed. The correlation between the actual value and the estimation indicates that the estimation produced by the non-transformed data is more accurate than the transformed data. The estimated results of each parameter are visualized in the form of a map so that changes in groundwater quality every year can be seen. Besides the maps, the results of this study are shown in graphs of changes in the form of cross-sections of each parameter from 2015 to 2019. Visualization of changes in the quality level groundwater is expected to give input for relevant agencies in the conservation of water resources. Keywords: Karst Mining, Mapping, Geostatistics, Groundwater Quality.
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Bernardi, A. C. C., G. M. Bettiol, G. G. Mazzuco, S. N. Esteves, P. P. A. Oliveira, and J. R. M. Pezzopane. "Spatial variability of soil fertility in an integrated crop livestock forest system." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 590–93. http://dx.doi.org/10.1017/s2040470017001145.
Повний текст джерелаАнотація:
Knowledge on spatial variability of soil properties is useful for the rational use of inputs, as in the site specific application of lime and fertilizer. Crop-livestock-forest integrated systems (CLFIS) provide a strategy of sustainable agricultural production which integrates annual crops, trees and livestock activities on a same area and in the same season. Since the lime and fertilizer are key factors for the intensification of agricultural systems in acid-soil in the tropics, precision agriculture (PA) is the tool to improve the efficiency of use of these issues. The objective of this research was to map and evaluate the spatial variability of soil properties, liming and fertilizer need of a CLFIS. The field study was carried out in a 30 ha area at Embrapa Pecuária Sudeste in São Carlos, SP, Brazil. Soil samples were collected at 0–0.2 m depth, and each sample represented a paddock. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms, the soil fertility information were modeled. Spatial variability soil properties and site specific liming and fertilizer need were modeled by kriging and inverse distance weighting (IDW) techniques. Another approach used was based on lime and fertilizer recommendation considering the paddocks as the minimum management unit. The results showed that geostatistics and GIS were useful tools for revealing soil spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application zones. Spatial analyses of crop needs and requirement can provide management tools for avoiding potential environmental problems, caused by unbalanced nutrient supplies.
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Han, Zhifeng, Jianxin Liao, Qi Qi, Haifeng Sun, and Jingyu Wang. "Radio Environment Map Construction by Kriging Algorithm Based on Mobile Crowd Sensing." Wireless Communications and Mobile Computing 2019 (February 3, 2019): 1–12. http://dx.doi.org/10.1155/2019/4064201.
Повний текст джерелаАнотація:
In the IoT era, 5G will enable various IoT services such as broadband access everywhere, high user and devices mobility, and connectivity of massive number of devices. Radio environment map (REM) can be applied to improve the utilization of radio resources for the access control of IoT devices by allocating them reasonable wireless spectrum resources. However, the primary problem of constructing REM is how to collect the large scale of data. Mobile crowd sensing (MCS), leveraging the smart devices carried by ordinary people to collect information, is an effective solution for collecting the radio environment information for building the REM. In this paper, we build a REM collecting prototype system based on MCS to collect the data required by the radio environment information. However, limited by the budget of the platform, it is hard to recruit enough participants to join the sensing task to collect the radio environment information. This will make the radio environment information of the sensing area incomplete, which cannot describe the radio information accuracy. Considering that the Kriging algorithm has been widely used in geostatistics principle for spatial interpolation for Kriging giving the best unbiased estimate with minimized variance, we utilize the Kriging interpolation algorithm to infer complete radio environment information from collected sample radio environment information data. The interpolation performance is analyzed based on the collected sample radio environment information data. We demonstrate experiments to analyze the Kriging interpolation algorithm interpolation results and error and compared them with the nearest neighbor (NN) and the inverse distance weighting (IDW) interpolation algorithms. Experiment results show that the Kriging algorithm can be applied to infer radio environment information data based on the collected sample data and the Kriging interpolation has the least interpolation error.
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Ly, S., C. Charles, and A. Degré. "Spatial interpolation of daily rainfall at catchment scale: a case study of the Ourthe and Ambleve catchments, Belgium." Hydrology and Earth System Sciences Discussions 7, no. 5 (September 27, 2010): 7383–416. http://dx.doi.org/10.5194/hessd-7-7383-2010.
Повний текст джерелаАнотація:
Abstract. Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (krigings) are widely used in spatial interpolation from point measurement to continuous surfaces. However, the majority of existing geostatistical algorithms are available only for single-moment data. The first step in kriging computation is the semi-variogram modelling which usually uses only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. In this study, we used daily rainfall data from 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, Cressie's Approximate Weighted Least Squares method was used to fit seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) to daily sample semi-variogram on a daily basis. Seven selected raingages were used to compare the interpolation performance of these algorithms applied to many degenerated-raingage cases. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably interpolation with the Thiessen polygon that is commonly used in various hydrological models. Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) presented the highest Root Mean Square Error (RMSE) between the geostatistical and IDW methods. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases.
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Amadu, Casmed Charles, Sampson Owusu, Gordon Foli, Blestmond A. Brako, and Samuel K. Abanyie. "COMPARISON OF ORDINARY KRIGING (OK) AND INVERSE DISTANCE WEIGHTING (IDW) METHODS FOR THE ESTIMATION OF A MODIFIED PALAEOPLACER GOLD DEPOSIT: A CASE STUDY OF THE TEBEREBIE GOLD DEPOSIT, SW GHANA." Malaysian Journal of Geosciences 6, no. 1 (2022): 19–28. http://dx.doi.org/10.26480/mjg.01.2022.19.28.
Повний текст джерелаАнотація:
The study described in this paper involves the application of a conventional resource estimation method, inverse distance weighting (IDW), and univariate geostatistical technique, ordinary kriging (OK) to the gold grades data from the modified palaeoplacer Teberebie gold deposit, in Ghana. The deposit consists of 4 layered well-defined orebodies referred to as A reef, CDE reef, F24 reef and G reef at the mine environment. Simple, reliable, and adequately accurate resource/reserve estimation are essential to mining operations. Data used for the research were collected by diamond and reverse circulation (RC) drilling. A total of 19353 one-meter composite samples, consisting of 18962 RC chip samples from 695 RC drill holes, and 391 diamond drill core samples from 11 DD holes. Samples were analysed by atomic absorption spectrometry (AAS) for gold (Au). Descriptive statistical treatment was conducted on grade values for the reefs. To analyse for spatial structure of Au mineralisation, experimental downhole, and several horizontal directional semi-variograms were computed, and models fitted. Ore reserves were estimated by OK and IDW methods, and results of the various reefs compared. Regression analysis of estimated results indicate that, the inverse distance square (ID2) model produced estimates that compared well with the OK model in all the ore zones. It is therefore, appropriate to use ID2 as an alternative estimation method to the OK method for purposes of mine planning and grade control.
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Thamsi, Alam Budiman, Izzulhaq Ainunnur, Habibie Anwar, and Muhammad Aswadi. "ESTIMASI SUMBERDAYA NIKEL MENGUNAKAN METODE INVERSE DISTANCE WEIGHT PT ANG AND FANG BROTHERS." JGE (Jurnal Geofisika Eksplorasi) 9, no. 1 (March 29, 2023): 5–17. http://dx.doi.org/10.23960/jge.v9i1.235.
Повний текст джерелаАнотація:
Estimasi sumberdaya nikel sangat penting diketahui agar dapat menjadi dasar dalam perencanaan tambang. Inverse Distance Weight (IDW) memiliki prinsip yaitu dengan dilakukan pembobotan titik data yang didasarkan pada penyebaran kualitas, blok yang akan ditaksir dari bobot penaksir, dan hubungan ruang letak terhadap bobot sampel. Tujuan penelitian ini agar mengetahui arah sebaran, jumlah sumberdaya nikel menggunakan IDW dan klasifikasi sumberdaya pada zona Cut Off Grade (COG) 1,3%. Pemilihan metode estimasi didasari analisis statistik dan geostatistik, jika koefisien variasi dibawah 1,5 dan selisih sill terhadap nugget diatas 50% maka penggunaan IDW baik digunakan. Metodologi penelitian ini menggunakan metode IDW yang didasari statistik dan geostatistik, berawal dari analisis statistik univarian, statistik spasial, statistik bivarian dan estimasi sumberdaya serta pengklasifikasian menggunakan standar Relative Kriging Standard Deviation (RKSD). Zona saprolit memiliki kualitas kadar diatas COG 1,3% dengan koefisien variasi 0,185 dan selisih sill dan nugget 72%, hubungan antar data aktual dan data spasial yaitu cukup baik karena koefisien korelasi bernilai 0,53 dan RMSE 0,28 yang artinya tingkat kesalahan data terbilang rendah. Arah sebaran nikel pada Blok Mawar PT. Aang and Fang Brothres ialah dari barat daya ke timur laut dengan jumlah sumberdaya menggunakan metode IDW didapatkan volume 239.882,81 m3, tonase 377.276,05 ton dan kadar rata-rata nikel 1,58 % dan zona saprolit merupakan sumberdaya terukur berdasarkan RKSD.
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Caloiero, Tommaso, Gaetano Pellicone, Giuseppe Modica, and Ilaria Guagliardi. "Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management." Applied Sciences 11, no. 20 (October 14, 2021): 9566. http://dx.doi.org/10.3390/app11209566.
Повний текст джерелаАнотація:
Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.
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Sadeghi, Seyyed Hadi, Hamid Nouri, and Mohammed Faramarzi. "Assessing the Spatial Distribution of Rainfall and the Effect of Altitude in Iran (Hamadan Province)." Air, Soil and Water Research 10 (January 1, 2017): 117862211668606. http://dx.doi.org/10.1177/1178622116686066.
Повний текст джерелаАнотація:
Atmospheric phenomena have an enormous influence on natural resources and human life. Lack of sufficient information and knowledge in regard to the management of natural areas and ecosystems will bring about a huge cost. Hamadan province according to the topography, geomorphology, and soil condition is known as a particular area. In this region, intense rains have brought about hillsides, runoffs, and floods annually destroying a large amount of foundations and eroding fertile soils. This study was conducted using rainfall average data from 35 synoptic stations and rain measurements between the years 1982 and 2012 (30 years). Geostatistical techniques are applied for zoning, such as kriging, co-kriging, inverse distance weighting (IDW), radial basis function, global polynomial interpolation, and local polynomial interpolation. For comparing and evaluating geostatistical methods, cross-validation and statistical parameters such as correlation coefficient and mean absolute relative error (MARE) were used. According to the results, it can be realized that simple co-kriging (exponential) technique with the highest correlation coefficient (.75) and the lowest MARE (.124) is the most appropriate geostatistical method to predict rainfall distribution. Also, it is realized that there is a correct correlation between the accuracy of co-kriging technique and elevation changes. However, IDW with power 5 is the least accurate technique.
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Bhowmik, Avit Kumar. "A Comparison of Bangladesh Climate Surfaces from the Geostatistical Point of View." ISRN Meteorology 2012 (November 29, 2012): 1–20. http://dx.doi.org/10.5402/2012/353408.
Повний текст джерелаАнотація:
This study analyses the degree and margin of differences among the surfaces of annual total precipitation in wet days (PRCPTOT) and the yearly maximum value of the daily maximum temperature (TXx) of Bangladesh, produced by thin plate spline (TPS), inverse distance weighting (IDW), ordinary kriging (OK), and universal kriging (UK) methods of spatial interpolation. From the surface differences, the maximum and minimum differences are observed between the surfaces produced by TPS and IDW, and OK and UK, respectively. The residual plots from cross-validation depict that IDW and OK methods mostly under predict and TPS and UK methods mostly overpredict the observed climate indices’ values. Both the tendency of methods’ over and underprediction and the surface-differences decrease with the increase in the number of available spatial point observations. Finally, two performance measures—the index of agreement (d) and the coefficient of variation of prediction (ρf)—imply that there is a little difference in the prediction ability of the four different methods. The performance of the spatial interpolation improves with the increase in the number of available spatial points, and eventually the predicted climate surfaces get similar. However, UK shows better interpolation performance in most of the years.
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Elaalem, Mukhtar Mahmud, Younes Daw Ezlit, and Asma Ali Elgmati. "Application of Inverse Distance Weighting in Mapping Some of Soil Chemical Properties in Ayn Hizam, Qaryat Batth and Taknis." Journal of Misurata University for Agricultural Sciences 2, no. 1 (September 8, 2020): 1–18. http://dx.doi.org/10.36602/jmuas.2020.v02.01.01.
Повний текст джерелаАнотація:
Determining variabilities of soil properties is important for ecological modelling, environmental predictions, precise agriculture, and management of natural resources. This study was aimed to examine Inverse distance weight (IDW) to predict the spatial variability of Exchangeable Sodium Percentage (ESP), Calcium Carbonate Percentage (% CaCO3) soil pH, Electrical conductivity and % Gypsum . The study area selected for this work consists of Ayn Hizam, Qaryat- Batth and Taknis. Data for 220 randomly distributed representing soil profiles were encoded in spreadsheets, 198 of them were used for predicting the spatial variability in the GIS environment for ESP, % CaCO3, soil pH, Electrical conductivity and % Gypsum. The rest of Data (i.e. 22 representative soil profiles) were utilized to evaluate the maps produced using Kriging or IDW methods. The results showed that using IDW method was trustable because the values of RMSE and R2 for all the IDW maps were within the acceptable range. The study suggested adopting the Geostatistical methods for studying spatial prediction for different soil proprieties. In addition, the study recommended updating soil data for the study area.
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Purnomo, Hendro. "COMPARISON THE PERFORMANCE OF ORDINARY KRIGING AND INVERSE DISTANCE WEIGHTING METHODS FOR MAPPING NICKEL LATERITE PROPERTIES." KURVATEK 4, no. 1 (June 25, 2019): 57–67. http://dx.doi.org/10.33579/krvtk.v4i1.1116.
Повний текст джерелаАнотація:
Pemilihan metode interpolasi yang sesuai untuk memprediksi kadar bijih pada lokasi yang tidak tersampel merupakan hal yang penting untuk pemetaan sebaran anomaly kadar dan estimasi sumberdaya. Tujuan penelitian ini dilakukan untuk mengevaluasi hasil estimasi metode ordinary kriging (OK) dan inverse distance weighting (IDW) dalam pemetaan distribusi dan potensi sumberdaya nikel (Ni) dan cobalt (Co) pada zona limonit dan saprolit. Dalam penelitian ini digunakan aplikasi perangkat lunak ArcGis 10.2 dengan Geostatistical Analyst Extention untuk menganalisis data. Untuk pemilihan model variogram dan interpolasi yang terbaik digunakan nilai parameter root mean square error (RMSE) yang diperoleh dari prosedur cross validation. Fitting variogram eksperimental dilakukan dengan model spherical, exponential dan gaussian, sedangkan pemilihan model variogram terbaik dilakukan berdasarkan nilai RMSE terkecil. Pada zona limonit, metode IDW dengan power 2 mempunyai performa terbaik untuk kadar Ni dan Co, sedangkan prosedur OK menghasilkan performa terbaik untuk ketebalan. Pada zona saprolit metode IDW dengan power 5 mempunyai performa terbaik untuk kadar Ni dan IDW power 1 menunjukkan performa terbaik pada kadar co dan ketebalan. Hasil interpolasi menunjukkan bahwa distribusi nikel dan potensi tambahan sumberdaya pada zona limonit dan saprolit masih terbuka ke arah timur laut dan barat daya daerah penelitian.Kata Kunci: ArcGIS, cross validation, IDW, OK, RMSE
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Idress, Muhammad, Yasmin Nergis, Ambreen Afzal, Samreen Riaz Ahmed, Viola Vambol, Sergij Vambol, Jawad Abdullah Butt, Mughal Sharif, and Sergiy Yeremenko. "Spatial-Temporal Analysis of Ambient Air Quality in Karachi through Geostatistical Interpolation (IDW) Technique." Journal of Environmental Accounting and Management 11, no. 4 (December 2023): 353–74. http://dx.doi.org/10.5890/jeam.2023.12.001.
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Santos, Carlos Antonio Costa dos, Oseas Machado Gomes, Francisco de Assis Salviano de Souza, and Willian De Paiva. "Análise Geoestatística da Precipitação Pluvial do Estado da Paraíba (Geostatistical Analysis of Precipitation in Paraiba State)." Revista Brasileira de Geografia Física 4, no. 4 (January 22, 2012): 692. http://dx.doi.org/10.26848/rbgf.v4i4.232712.
Повний текст джерелаАнотація:
Este trabalho tem como objetivo estudar a variabilidade espacial da precipitação pluvial média mensal do Estado da Paraíba, no período de 1962 a 2001, utilizando técnicas geoestatísticas. Os dados foram cedidos pela Unidade Acadêmica de Ciências Atmosféricas (UACA) da Universidade Federal de Campina Grande (UFCG), coletados em estações meteorológicas e postos pluviométricos localizados em 102 municípios do Estado. Foi usado o semivariograma para determinar a dependência espacial da variável em estudo, em seguida foi feita uma análise descritiva dos dados a fim de resumir a série e descrevê-la em termos numéricos. De acordo com os resultados obtidos todos os variogramas apresentaram forte dependência espacial (IDE ≥ 75%), com exceção dos meses de junho a setembro que apresentaram tendências nas séries e dificuldade no ajuste dos modelos de comportamento espacial. De acordo com a análise descritiva dos dados os coeficientes de variação apresentaram alta dispersão (Cv > 20%) entre os valores, indicando grande variabilidade da chuva. Também foram confeccionados mapas de krigagem ordinária para os meses de janeiro, fevereiro, outubro e novembro. Tomaram-se como critério os valores dos coeficientes de determinação (R2 > 93%) para se obter os mapas ajustados pelos modelos e os mapas dos resíduos. Palavras – Chave: Geoestatística, precipitação e krigagem ordinária. Geostatistical Analysis of Precipitation in Paraiba State ABSTRACTThis work aims to study the spatial variability of monthly average rainfall of the State of Paraíba, in the period 1962 to 2001, using geostatistical techniques. The data were provided by the Academic Unit of Atmospheric Sciences (UACA), Federal University of Campina Grande (UFCG), collected from weather stations and climatic stations located in 102 municipalities in the State. Semivariogram was used to determine the spatial dependence of the variable under study was then carried out a descriptive analysis to summarize the series and describe it in numerical terms. According to the results of all variograms showed a strong spatial dependence (IDE ≥ 75%), with the exception of the months from June to September which showed trends in the series and difficulty in adjusting the models of spatial behavior. According to the data descriptive analysis coefficients of variation showed a high dispersion (CV > 20%) between the values, indicating great variability in rainfall. Were also made maps of ordinary kriging for the months of January, February, October and November. Were taken as criteria, the coefficients of determination (R2 > 93%) to obtain the maps set by models and residual maps. Keywords: Geostatistics, kriging and precipitation.
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Xia, Fang, Bifeng Hu, Youwei Zhu, Wenjun Ji, Songchao Chen, Dongyun Xu, and Zhou Shi. "Improved Mapping of Potentially Toxic Elements in Soil via Integration of Multiple Data Sources and Various Geostatistical Methods." Remote Sensing 12, no. 22 (November 17, 2020): 3775. http://dx.doi.org/10.3390/rs12223775.
Повний текст джерелаАнотація:
Soil pollution by potentially toxic elements (PTEs) has become a core issue around the world. Knowledge of the spatial distribution of PTEs in soil is crucial for soil remediation. Portable X-ray fluorescence spectroscopy (p-XRF) provides a cost-saving alternative to the traditional laboratory analysis of soil PTEs. In this study, we collected 293 soil samples from Fuyang County in Southeast China. Subsequently, we used several geostatistical methods, such as inverse distance weighting (IDW), ordinary kriging (OK), and empirical Bayesian kriging (EBK), to estimate the spatial variability of soil PTEs measured by the laboratory and p-XRF methods. The final maps of soil PTEs were outputted by the model averaging method, which combines multiple maps previously created by IDW, OK, and EBK, using both lab and p-XRF data. The study results revealed that the mean PTE content measured by the laboratory methods was as follows: Zn (127.43 mg kg−1) > Cu (31.34 mg kg−1) > Ni (20.79 mg kg−1) > As (10.65 mg kg−1) > Cd (0.33 mg kg−1). p-XRF measurements showed a spatial prediction accuracy of soil PTEs similar to that of laboratory analysis measurements. The spatial prediction accuracy of different PTEs outputted by the model averaging method was as follows: Zn (R2 = 0.71) > Cd (R2 = 0.68) > Ni (R2 = 0.67) > Cu (R2 = 0.62) > As (R2 = 0.50). The prediction accuracy of the model averaging method for five PTEs studied herein was improved compared with that of the laboratory and p-XRF methods, which utilized individual geostatistical methods (e.g., IDW, OK, EBK). Our results proved that p-XRF was a reliable alternative to the traditional laboratory analysis methods for mapping soil PTEs. The model averaging approach improved the prediction accuracy of the soil PTE spatial distribution and reduced the time and cost of monitoring and mapping PTE soil contamination.
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Mishra, Rahul, S. P. Datta, M. C. Meena, D. Golui, K. K. Bandyopadhyay, A. Bhatia, and A Chaudhary. "Geostatistical analysis of arsenic contamination in soil and comparison of interpolation techniques in Nadia district of Bengal, India." Emergent Life Sciences Research 09, no. 01 (2023): 83–90. http://dx.doi.org/10.31783/elsr.2023.918390.
Повний текст джерелаАнотація:
The contamination of soil and water with arsenic directly or indirectly affects millions of people, particularly in Southeast Asia. Efficiently managing contaminated sites cost-effectively requires an understanding of the spatial distribution of contamination in soil. In this study, different interpolation methods, including Ordinary Kriging (OK), Inverse Distance Weighted (IDW), Radial Basis Function (RBF), and Empirical Bayesian Kriging (EBK), were evaluated in the Bengal region to determine their effectiveness in predicting the Olsen extractable As content in the soil. The study found that the mean Olsen extractable content in soil was 1.45 mg kg-1 , with a range of 0.48 to 3.57 mg kg-1 . Geostatistical analysis showed that the northern side of Nadia had relatively high contamination, while the southern side had relatively lower contamination. The Root Mean Square Error (RMSE) values of the different interpolation methods ranged from 0.52 to 0.54, with corresponding mean cross-validation (CV) values ranging from -0.005 to 0.008. The predicted minimum and maximum values of as-in soil were in close agreement with the measured values for IDW interpolation, followed by OK, RBF, and EBK. The study found that IDW consistently provided the most precise predictions of pollution in the soil throughout space. These findings have significant implications for managing contamination in the Nadia West Bengal and other regions facing similar challenges.
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Hong, Mei, Ren Zhang, Dong Wang, Longxia Qian, and Zhenghua Hu. "Spatial Interpolation of Annual Runoff in Ungauged Basins Based on the Improved Information Diffusion Model Using a Genetic Algorithm." Discrete Dynamics in Nature and Society 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/4293731.
Повний текст джерелаАнотація:
Prediction in Ungauged Basins (PUB) is an important task for water resources planning and management and remains a fundamental challenge for the hydrological community. In recent years, geostatistical methods have proven valuable for estimating hydrological variables in ungauged catchments. However, four major problems restrict the development of geostatistical methods. We established a new information diffusion model based on genetic algorithm (GIDM) for spatial interpolating of runoff in the ungauged basins. Genetic algorithms (GA) are used to generate high-quality solutions to optimization and search problems. So, using GA, the parameter of optimal window width can be obtained. To test our new method, seven experiments for the annual runoff interpolation based on GIDM at 17 stations on the mainstream and tributaries of the Yellow River are carried out and compared with the inverse distance weighting (IDW) method, Cokriging (COK) method, and conventional IDMs using the same sparse observed data. The seven experiments all show that the GIDM method can solve four problems of the previous geostatistical methods to some extent and obtains best accuracy among four different models. The key problems of the PUB research are the lack of observation data and the difficulties in information extraction. So the GIDM is a new and useful tool to solve the Prediction in Ungauged Basins (PUB) problem and to improve the water management.
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Nejad, Parviz Ghojogh, Anuar Ahmad, and Irina Safitri Zen. "ASSESSMENT OF THE INTERPOLATION TECHNIQUES ON TRAFFIC NOISE POLLUTION MAPPING FOR THE CAMPUS ENVIRONMENT SUSTAINABILITY." International Journal of Built Environment and Sustainability 6, no. 1-2 (April 1, 2019): 147–59. http://dx.doi.org/10.11113/ijbes.v6.n1-2.393.
Повний текст джерелаАнотація:
Mapping traffic noise pollution from an increasing number of vehicles facilitate better land use planning in order to measures the environment sustainability performances of institution in higher education. The aim of this research is to analyse the relationship on the increasing number of the motorized vehicles recorded as noise pollution data for further geostatistical analysis. Hence, by using the interpolation techniques, Kriging and IWD, the comparison performed to particularly create the noise pollution map for Universiti Teknologi Malaysia, UTM. With average noise of the collected sample, the performance of two methods; inverse distance weighting, IDW and Kriging evaluated based on the magnitude and distribution of errors where the cross-validation statistics with plots shows IDW better representation of reality for the means of Noise pollution levels measurement. then, other the noise map generated based on the maximum noise level recorded with the indicator Kriging Noise method. Further, these studies can be useful to conduct regular assessments to identify noise pollution level with multiple locations in the study area.
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Jumaah, Huda Jamal, Mohammed Hashim Ameen, Bahareh Kalantar, Hossein Mojaddadi Rizeei, and Sarah Jamal Jumaah. "Air quality index prediction using IDW geostatistical technique and OLS-based GIS technique in Kuala Lumpur, Malaysia." Geomatics, Natural Hazards and Risk 10, no. 1 (January 1, 2019): 2185–99. http://dx.doi.org/10.1080/19475705.2019.1683084.
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Borrego-Alonso, David, Antonio M. Martínez-Graña, Begoña Quintana, and Juan Carlos Lozano. "From Spatial Characterisation to Prediction Maps of the Naturally Occurring Radioactivity in Groundwaters Intended for Human Consumption of Duero Basin, Castilla y León (Spain)." Agronomy 12, no. 9 (August 29, 2022): 2059. http://dx.doi.org/10.3390/agronomy12092059.
Повний текст джерелаАнотація:
According to the European Council Directive 51/2013 EURATOM, the radionuclide content in human consumption waters must comply with the stated recommendations to ensure the protection of public health. The radiological characterisation assessed in Laboratorio de Radiaciones Ionizantes y Datación of Universidad de Salamanca, in more than 400 groundwater samples gathered from intakes intended for human consumption from the Castilla y León region (Spain), has provided worthwhile data for evaluating the spatial distribution of the radioactivity content in the Duero basin. For this purpose, geostatistical exploration and interpolation analysis, using the inverse distance weighting (IDW) method, was performed. The IDW prediction maps showed higher radioactivity occurrence in western and southern areas of the study region, mainly related to the mineralogical influence of the igneous and metamorphosed outcroppings in the Cenozoic sediments that formed the porous detritic aquifers of the Duero basin edges. The hydraulic characteristics promote the distribution of these radioactivity levels towards the centre of the basin as a consequence of the unconfined nature of the aquifers. Prediction maps provide a worthwhile tool that can be used for better planning and managing of groundwater monitoring programmes.
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Bargawa, Waterman Sulistyana, Hadi Oetomo, Sri Harjanti, Oktarian Wisnu Lusantono, Hadi Zulkarnain Ladianto, and M. Anwar Safi’i. "Classification of Laterite Nickel Resources Using the Average Distance Approach." RSF Conference Series: Engineering and Technology 1, no. 1 (December 23, 2021): 105–12. http://dx.doi.org/10.31098/cset.v1i1.379.
Повний текст джерелаАнотація:
The resource classification system helps protect producers and consumers from ambiguous reporting of mineral resources. Classification systems have been introduced in many countries, but they are often general, so they are not easy to apply in the field. Geostatistical approaches are often inaccurate on data with high nugget values. The system requires sophisticated knowledge and takes time to understand, while field practitioners are eager to immediately get mineral resource classification results. This study aims to introduce the average distance from the borehole as a mineral resource classification parameter. In this study, modeling and grade estimation uses a block model with nearest-neighbor polygon and inverse distance weighing techniques as grade estimation techniques. The highest weight in the NNP estimation technique is the closest sample, while the IDW weight depends on the distance; therefore the NNP and IDW techniques use distance considerations only. Based on the histogram of the average distance, the populations in the graph show the classification as inferred resources, indicated resources, and measured resources. The application of the average distance technique for the classification of laterite nickel resources uses the block model.
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Salekin, Serajis, Jack Burgess, Justin Morgenroth, Euan Mason, and Dean Meason. "A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation." ISPRS International Journal of Geo-Information 7, no. 8 (July 27, 2018): 300. http://dx.doi.org/10.3390/ijgi7080300.
Повний текст джерелаАнотація:
It is common to generate digital elevation models (DEMs) from aerial laser scanning (ALS) data. However, cost and lack of knowledge may preclude its use. In contrast, global navigation satellite systems (GNSS) are seldom used to collect and generate DEMs. These receivers have the potential to be considered as data sources for DEM interpolation, as they can be inexpensive, easy to use, and mobile. The data interpolation method and spatial resolution from this method needs to be optimised to create accurate DEMs. Moreover, the density of GNSS data is likely to affect DEM accuracy. This study investigates three different deterministic approaches, in combination with spatial resolution and data thinning, to determine their combined effects on DEM accuracy. Digital elevation models were interpolated, with resolutions ranging from 0.5 m to 10 m using natural neighbour (NaN), topo to raster (ANUDEM), and inverse distance weighted (IDW) methods. The GNSS data were thinned by 25% (0.389 points m−2), 50% (0.259 points m−2), and 75% (0.129 points m−2) and resulting DEMs were contrast against a DEM interpolated from unthinned data (0.519 points m−2). Digital elevation model accuracy was measured by root mean square error (RMSE) and mean absolute error (MAE). It was found that the highest resolution, 0.5 m, produced the lowest errors in resulting DEMs (RMSE = 0.428 m, MAE = 0.274 m). The ANUDEM method yielded the greatest DEM accuracy from a quantitative perspective (RMSE = 0.305 m and MAE = 0.197 m); however, NaN produced a more visually appealing surface. In all the assessments, IDW showed the lowest accuracy. Thinning the input data by 25% and even 50% had relatively little impact on DEM quality; however, accuracy decreased markedly at 75% thinning (0.129 points m−2). This study showed that, in a time where ALS is commonly used to generate DEMs, GNSS-surveyed data can be used to create accurate DEMs. This study confirmed the need for optimization to choose the appropriate interpolation method and spatial resolution in order to produce a reliable DEM.
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Benslama, Abderraouf, Kamel Khanchoul, Fouzi Benbrahim, Sana Boubehziz, Faredj Chikhi, and Jose Navarro-Pedreño. "Monitoring the Variations of Soil Salinity in a Palm Grove in Southern Algeria." Sustainability 12, no. 15 (July 29, 2020): 6117. http://dx.doi.org/10.3390/su12156117.
Повний текст джерелаАнотація:
Soil salinity is considered the most serious socio-economic and environmental problem in arid and semi-arid regions. This study was done to estimate the soil salinity and monitor the changes in an irrigated palm grove (42 ha) that produces dates of a high quality. Topsoil samples (45 points), were taken during two different periods (May and November), the electrical conductivity (EC) and Sodium Adsorption Ratio (SAR) were determined to assess the salinity of the soil. The results of the soil analysis were interpolated using two geostatistical methods: inverse distance weighting (IDW) and ordinary Kriging (OK). The efficiency and best model of these two methods was evaluated by calculating the mean error (ME) and root mean square error (RMSE), showing that the ME of both interpolation methods was satisfactory for EC (−0.003, 0.145) and for SAR (−0.03, −0.18), but the RMSE value was lower using the IDW with both data and periods. This can explain the accuracy of the IDW interpolation method. This model showed a dominance of soil salinity distribution in the South and South-East of the study area during the first season, and for the second season, the salts were concentrated in the middle of the area. Several factors could interact in this variation such as the topographic direction of the water flow and the aridity of the climate (evaporation). From this study emerges the need to maintain a better management of agricultural water and soils, avoiding salt accumulation, to ensure a good yield and the sustainability of agriculture in arid environments.
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Bhattarai, A., S. Shrestha, and R. P. Regmi. "Spatial Distribution of Groundwater Level over Lowlands of Morang District of Eastern-Nepal." Journal of Nepal Physical Society 8, no. 3 (December 30, 2022): 39–44. http://dx.doi.org/10.3126/jnphyssoc.v8i3.50724.
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A better understanding of spatial distribution of groundwater level is necessary for the development of groundwater development strategies and sustainable use of available resources. The present study evaluates the spatial distribution of groundwater level over the Morang Administrative District of Eastern Nepal and the groundwater accessibility over the area. The study was realized by performing an extensive field survey of groundwater level during the post-monsoon season over 126 sites. The study area of 1224.80 km2 was gridded at 3 km x 3 km horizontal grid resolution and at least one survey was ensured for each grid point. Downscaled spatial distribution of groundwater level was achieved by interpolating the observed data using the Inverse Distance Weighting (IDW) with different weighting parameters available with the geostatistical module of ArcGIS. The performances of the interpolation methods were evaluated based on the cross-validation of results characterized by the statistical parameters RMSE, R2 and MAE and optimal power (α) for weighting function. The IDW (with α=4) appears to perform well for the study area with Coefficient of Determination (R2) and Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values of 0.336, 4.750 and 2.967, respectively. The spatial distribution of groundwater level over the low-lands of Morang District mapped with the IDW interpolation method, revealed that the ground water level is maximum in south-western part of the district. The depth to groundwater lies between the ranges of 4.5 to 10 meters covering almost 67.47% of the total study area.
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Fu, Tonggang, Hui Gao, and Jintong Liu. "Comparison of Different Interpolation Methods for Prediction of Soil Salinity in Arid Irrigation Region in Northern China." Agronomy 11, no. 8 (July 30, 2021): 1535. http://dx.doi.org/10.3390/agronomy11081535.
Повний текст джерелаАнотація:
Numerous methods have been used in the spatial prediction of soil salinity. However, the most suitable method is still unknown in arid irrigation regions. In this paper, 78 locations were sampled in salt-affected land caused by irrigation in an arid area in northern China. The geostatistical characteristics of the soil pH, Sodium Adsorption Ratio (SAR), Total Salt Content (TSC), and Soil Organic Matter (SOM) of the surface (0–20 cm) and subsurface (20–40 cm) layers were analyzed. The abilities of the Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and CoKriging (CK) interpolation methods were compared, and the Root Mean Square Error (RMSE) was used to justify the results of the methods. The results showed that the spatial distributions of the soil properties obtained using the different interpolation methods were similar. However, the surface layer exhibits more spatial heterogeneity than the subsurface layer. Based on the RSME, the nugget/sill value and range significantly affected which method was the most suitable. Lower nugget/sill values and lower ranges can be fitted using the IDW method, but higher nugget/sill values and higher ranges can be fitted using the OK method. These results provide a valuable reference for the prediction of soil salinity.
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Mendez, Maikel, Luis-Alexander Calvo-Valverde, Ben Maathuis, and Luis-Fernando Alvarado-Gamboa. "Generation of Monthly Precipitation Climatologies for Costa Rica Using Irregular Rain-Gauge Observational Networks." Water 11, no. 1 (January 3, 2019): 70. http://dx.doi.org/10.3390/w11010070.
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Precipitation climatologies for the period 1961–1990 were generated for all climatic regions of Costa Rica using an irregular rain-gauge observational network comprised by 416 rain-gauge stations. Two sub-networks were defined: a high temporal resolution sub-network (HTR), including stations having at least 20 years of continuous records during the study period (157 in total); and a high spatial resolution sub-network (HSR), which includes all HTR-stations plus those stations with less than 20 years of continuous records (416 in total). Results from the kriging variance reduction efficiency (KRE) objective function between the two sub-networks, show that ordinary kriging (OK) is unable to fully explain the spatio-temporal variability of precipitation within most climatic regions if only stations from the HTR sub-network are used. Results also suggests that in most cases, it is beneficial to increase the density of the rain-gauge observational network at the expense of temporal fidelity, by including more stations even though their records may not represent the same time step. Thereafter, precipitation climatologies were generated using seven deterministic (IDW, TS2, TS2PARA, TS2LINEAR, TPS, MQS and NN) and two geostatistical (OK and KED) interpolation methods. Performance of the various interpolation methods was evaluated using cross validation technique, selecting the mean absolute error (MAE) and the root-mean square error (RMSE) as agreement metrics. Results suggest that IDW is marginally superior to OK and KED for most climatic regions. The remaining deterministic methods however, considerably deviate from IDW, which suggests that these methods are incapable of properly capturing the true-nature of spatial precipitation patterns over the considered climatic regions. The final generated IDW climatology was then validated against the Global Precipitation Climatology Centre (GPCC), Climate Research Unit (CRU) and WorldClim datasets, in which overall spatial and temporal coherence is considered satisfactory, giving assurance about the use this new climatology in the development of local climate impact studies.
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VIANA, LILIANE DA SILVA, MARYJANE DINIZ DE ARAÚJO GOMES, ÉLVIS DA SILVA ALVES, ALEX PAULO MARTINS DO CARMO, VERA QUEIROZ DE SOUZA, and LAILSON DA SILVA FREITAS. "VARIABILIDADE TEMPORAL DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA PARA A MESORREGIÃO METROPOLITANA DE BELÉM-PA." IRRIGA 27, no. 1 (March 28, 2022): 111–23. http://dx.doi.org/10.15809/irriga.2022v27n1p111-123.
Повний текст джерелаАнотація:
VARIABILIDADE TEMPORAL DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA PARA A MESORREGIÃO METROPOLITANA DE BELÉM-PA LILIANE DA SILVA VIANA1; MARYJANE DINIZ DE ARAÚJO GOMES2; ÉLVIS DA SILVA ALVES3; ALEX PAULO MARTINS DO CARMO4; VERA QUEIROZ DE SOUZA5; LAILSON DA SILVA FREITAS6 1Engenheira Agrônoma, Instituto Federal do Pará – Campus Castanhal – PA – BR 316, Km 61, Saudade II, CEP: 68740-970, Castanhal – PA, Brasil, Email: liliane.agro.viana@gmail.com 2Professora, Instituto Federal do Pará – Campus Castanhal – PA – BR 316, Km 61, Saudade II, CEP: 68740-970, Castanhal – PA, Brasil, Email: gomes-mary@hotmail.com 3Doutor em Engenharia Agrícola, Brasil, Email: elvistv@gmail.com 4Doutorando em Produção Vegetal, Universidade Estadual do Norte Fluminense Darcy Ribeiro– Campos dos Goytacazes – RJ, Av. Alberto Lamego, 2000, Parque Califórnia, CEP: 28013-602, Campos dos Goytacazes – RJ, Brasil, Email: alex.taa97@gmail.com 5Graduanda em Agronomia, Instituto Federal do Pará – Campus Castanhal – PA – BR 316, Km 61, Saudade II, CEP: 68740-970, Castanhal – PA, Brasil, Email: veraqueirozsouza95@gmail.com 6Mestrando em Produção Vegetal, Universidade Estadual do Norte Fluminense Darcy Ribeiro– Campos dos Goytacazes – RJ, Av. Alberto Lamego, 2000, Parque Califórnia, CEP: 28013-602, Campos dos Goytacazes – RJ, Brasil, Email: lailsonfreitas222@gmail.com 1 RESUMO A evapotranspiração de referência (ETo) é diretamente influenciada por variáveis climatológicas e seu estudo é importante para a agricultura irrigada, pois evidencia a quantidade de água que se desloca para a atmosfera, possibilitando compreender a demanda hídrica das culturas. Neste sentido, o objetivo do trabalho foi estimar, através de análise geoestatística, a variabilidade temporal da ETo para a região metropolitana de Belém, PA. O estudo foi realizado com base em dados climatológicos da estação convencional de Belém em um período de 12 anos, obtidos do Banco de Dados Meteorológicos para Ensino e Pesquisa (BDMEP) do Instituto Nacional de Meteorologia (INMET). A ETo foi calculada pelo software REF-ET 4.1.22, a estatística descritiva por meio de uma planilha eletrônica e a análise geoestatística pelo software GS+®. A ETo na região metropolitana de Belém, aumentou gradativamente a partir de 2007, sendo influenciada pelas mudanças climáticas que ocorrem nessa região. Constatou-se, nos doze anos avaliados, que apenas três apresentaram moderado índice de dependência temporal (IDT) e os demais, forte IDT. Analisando os mapas de distribuição temporal, foi possível constatar que os meses de maior demanda evapotranspirativa foram agosto, setembro, outubro e novembro e que houve um incremento anual de 1,0 mm dia-1 da ETo. Palavras-chave: Geoestatística, irrigação, agroclimatologia, temperatura. VIANA, L. S.; GOMES, M. D. A.; ALVES, E. S.; CARMO, A. P. M.; SOUZA, V. Q.; FREITAS, L. S. TEMPORAL VARIABILITY OF REFERENCE EVAPOTRANSPIRATION FOR THE METROPOLITAN MESORREGIÃO OF BELÉM-PA 2 ABSTRACT The reference evapotranspiration (ETo) is directly influenced by climatological variables and its study is important for irrigated agriculture since it shows the amount of water that moves into the atmosphere, making it possible to understand crop water demand. this sense, this work aimed to estimate, through geostatistical analysis, the temporal variability of ETo for the metropolitan region of Belém, PA. The study was conducted based on climatological data from the conventional station of Belém for 12 years, obtained from the Meteorological Database for Teaching and Research (BDMEP) of the National Institute of Meteorology (INMET). The ETo was calculated using the REF-ET 4.1.22 software, descriptive statistics through an electronic spreadsheet, and geostatistical analysis by the GS + ® software. The ETo in the metropolitan region of Belém, PA, increased gradually from 2007, being influenced by the climatic changes that occur in this region. In the twelve years evaluated, it was found that only three had a moderate temporal dependency index (IDT) and the others had a strong IDT. Analyzing the temporal distribution maps, it was possible to verify that the months of greatest evapotranspirative demand were August, September, October, and November and that there was an annual increase of 1.0 mm day-1 of ETo. Keywords: Geostatistics, irrigation, agroclimatology, temperature.
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Issazadeh, Lida, Mustafa Ismail Umar, Said I. A. Al-Sulaivany, and Jian Hassanpour. "Geostatistical Analysis of the Permeability Coefficient in Different Soil Textures." Contemporary Agriculture 67, no. 2 (July 1, 2018): 119–24. http://dx.doi.org/10.2478/contagri-2018-0017.
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Summary Estimating soil hydraulic properties are so important for hydrological modeling, designing irrigation-drainage systems and soil transmission of soluble salts and pollutants, although measurements of such parameters have been found costly and time-consuming. Owing to a high spatial variability of soil hydraulic characteristics, a large number of soil samples are required for proper analysis. Nowadays, geostatistical methods are used to estimate soil parameters on the basis of limited data. The purpose of this research is to investigate the spatial variability of the permeability coefficient in different soil textures (26 soil samples) found in the Kurdistan region of Iraq. The parameter values obtained indicated a normal trend in particle size distribution, whereas the values of permeability coefficient showed aberrant distribution patterns. Geostatistical analysis results indicated the best fitted theoretical model was Gaussian model and the proportion of sill/(sill + nugget) was 0.17 indicated strong spatial dependency of soil permeability. Furthermore, the optimal distance for estimating the soil permeability coefficient was 109,119 meters. A comparison of the kriging and IDW interpolation methods showed that both methods can estimate soil permeability with high accuracy and less error. The prediction maps of the applied methods indicated that high soil permeability rates were recorded in the south-east of the Kurdistan region of Iraq compared to low soil permeability rates recorded in the remainder of this region. It is recommended other interpolation methods such as co-kriging and indicator or simple kriging methods could be used to simulate data in large scale areas as well.
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Myslyva, Tamara, Olesya Kutsaeva, and Natallia Krundzikava. "Efficiency of Interpolation Methods Based on GIS for Estimating of Spatial Distribution of PH in Soil." Baltic Surveying 11 (November 20, 2019): 53–59. http://dx.doi.org/10.22616/j.balticsurveying.2019.017.
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The main objective of this study is to review and evaluate three common interpolation methods namely: Inverse Distance Weighting (IDW), Radial Basis Function (RBF) and Ordinary Kriging (OK), and generate maps of soil pH using these methods. The accuracy and efficiency of the generated maps have been examined as well as the most fitting technique for estimating spatial distribution of soil pH in the study area is identified. Studies were conducted within the limits of land use of RUP “Uchkhoz BGSHA” (Republic of Belarus, Mogilev region, Goretsky district). The total area of the surveyed territory is 3197.89 hectares. For the analysis data is used about pHKCl of soil solution obtained from materials of an agrochemical survey executed in 2014. Forecasting and visualization of the spatial distribution of pHKCl was carried out using the Geostatistical Analyst module of the ArcGIS software. The experimental anisotropic variograms were calculated to determine the possible spatial structure of soil pH. Based on cross-validation results, a polynomial function was identified as the best variogram model. The model created by the method of radial basis functions turned out to be the most suitable for forecasting purposes (the value of the root-mean-square error was 0.763). In terms of interpolation accuracy, the investigated deterministic and geostatistical methods are located in the next descending row
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Zhang, Yufu, Xinyi Jiao, Yinghuai Wei, Hao Wu, Zheqi Pan, Mei Liu, Julin Yuan, et al. "Long-Term (1990–2013) Changes and Spatial Variations of Cropland Runoff across China." Water 14, no. 18 (September 17, 2022): 2918. http://dx.doi.org/10.3390/w14182918.
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Quantitative information on regional cropland runoff is important for sustainable agricultural water quantity and quality management. This study combined the Soil Conservation Service Curve Number (SCS-CN) method and geostatistical approaches to quantify long-term (1990–2013) changes and regional spatial variations of cropland runoff in China. Estimated CN values from 17 cropland study sites across China showed reasonable agreement with default values from the National Engineering Handbook (R2 = 0.76, n = 17). Among four commonly used geostatistical interpolation methods, the inverse distance weighting (IDW) method achieved the highest accuracy (R2 = 0.67, n = 209) for prediction of cropland runoff. Using default CN values and the IDW method, estimated national annual cropland runoff volume and runoff depth in 1990–2013 were 253 ± 25 km3 yr−1 and 182 ± 15 mm yr−1, respectively. Estimated cropland runoff depth gradually increased from the drier northwest inland region to the wetter southeast coastal region (range: 2–1375 mm yr−1). Regionally, eastern, central and southern China accounted for 39% of the cultivated area and 53% of the irrigated land area and contributed to 68% of the national cropland runoff volume. In contrast, northwestern, northern, southwestern and northeastern China accounted for 61% of the cultivated area and 47% of the irrigated land area and contributed to 32% of the runoff volume. Rainfall was the main source (72%) of cropland runoff for the entire nation, while irrigation became the main source of cropland runoff in drier regions (northwestern and southwestern China). Over the 24-year study period, estimated cropland runoff depth showed no significant trends, whereas cropland runoff volume and irrigation-contributed percentages decreased by 7% and 35%, respectively, owing to implementation of water-saving irrigation technologies. To reduce excessive runoff and increase water utilization efficiencies, regionally specific water management strategies should be further promoted. As the first long-term national estimate of cropland runoff in China, this study provides a simple framework for estimating regional cropland runoff depth and volume, providing critical information for guiding developments of management practices to mitigate agricultural nonpoint source pollution, soil erosion and water scarcity.
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Indarto, I. "Aplikasi ESDA untuk Studi Variabilitas Spasial Hujan Bulanan di Jawa Timur." Forum Geografi 25, no. 2 (December 20, 2011): 178. http://dx.doi.org/10.23917/forgeo.v25i2.5044.
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This article expose the spatial variability of monthly-rainfall (MR) in East Java region. Monthly rainfall data were collected from 943 pluviometres spread around the regions. Spatial statistics analysed by means of ESDA (Exploratory Spatial Data Analysis) techniques available on Geostatistical Analyst extention of ArcGIS (9.3). Statistical tools exploited to analise the data include: (1) Histogram, (2) Voronoi Map, and (3) QQ-Plot. The result show that histogram and QQ-Plot of Monthly Rainfall data are leptocurtosis. Statistical value obtained from the analysis are: minimum = 54 mm/month, average = 155,5 mm/month, maximum = 386 mm/month, and median = 150 mm/month. Other statistical value summarised are: standard deviation = 44,2 ; skewness = 0,95; and curtosis = 5,09. Finally, monthly rainfall-maps are produced by interpolating the data using Inverse Distance Weighed (IDW) interpolation method. The research demonstrate the capability and benefit of those statistical tool to describe detailed spatial variability of rainfall.
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Putra, Ahmad Pratama. "SPATIAL PATTERN OF SULFUR DIOXIDE DISPERSION AND AFFECTED SETTLEMENT AREAS IN SERANG REGENCY BANTEN PROVINCE." Jurnal Sains dan Teknologi Mitigasi Bencana 15, no. 1 (June 30, 2020): 27–39. http://dx.doi.org/10.29122/jstmb.v15i1.4117.
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In Serang Regency, pollutants and air emissions are produced by industrial activities, including Sulfur Dioxide (SO2) gas. SO2-polluted air causes problems to the human respiratory system. This study examine the spatial pattern of SO2 gas dispersion and its impact on settlements in Serang Regency using geostatistic and Inverse Distance Weighting (IDW) method in Arc GIS. Based on the results of ambient air quality measurements in Serang Regency, which have been measured by the Serang Regency Environmental Agency from 2015 - 2019 for the SO2 parameter, it can be seen that none of them exceed the standard of the PP No. 411999 concerning Air Pollution Control. The results of spatial analysis of gas dispersion show tendencies of high gas concentration in industrial zones, which indicate the contribution of gases from industrial business activities in several monsoons. The most extensive settlement affected by SO2 gas with the highest value of 40-50 µg / Nm3 is on Cikande District covering an area of 3.173,77 Ha which occurs during the west monsoon from December to February.
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Usowicz, Bogusław, Jerzy Lipiec, Mateusz Łukowski, and Jan Słomiński. "Improvement of Spatial Interpolation of Precipitation Distribution Using Cokriging Incorporating Rain-Gauge and Satellite (SMOS) Soil Moisture Data." Remote Sensing 13, no. 5 (March 9, 2021): 1039. http://dx.doi.org/10.3390/rs13051039.
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Precipitation data provide a crucial input for examining hydrological issues, including watershed management and mitigation of the effects of floods, drought, and landslides. However, they are collected frequently from the scarce and often insufficient network of ground-based rain-gauge stations to generate continuous precipitation maps. Recently, precipitation maps derived from satellite data have not been sufficiently linked to ground-based rain gauges and satellite-derived soil moisture to improve the assessment of precipitation distribution using spatial statistics. Kriging methods are used to enhance the estimation of the spatial distribution of precipitations. The aim of this study was to assess two geostatistical methods, ordinary kriging (OK) and ordinary cokriging (OCK), and one deterministic method (i.e., inverse distance weighting (IDW)) for improved spatial interpolation of quarterly and monthly precipitations in Poland and near-border areas of the neighbouring countries (~325,000 or 800,000 km2). Quarterly precipitation data collected during a 5-year period (2010–2014) from 113–116 rain-gauge stations located in the study area were used. Additionally, monthly precipitations in the years 2014–2017 from over 400 rain-gauge stations located in Poland were used. The spatiotemporal data on soil moisture (SM) from the Soil Moisture and Ocean Salinity (SMOS) global satellite (launched in 2009) were used as an auxiliary variable in addition to precipitation for the OCK method. The predictive performance of the spatial distribution of precipitations was the best for OCK for all quarters, as indicated by the coefficient of determination (R2 = 0.944–0.992), and was less efficient (R2 = 0.039–0.634) for the OK and IDW methods. As for monthly precipitation, the performance of OCK was considerably higher than that of IDW and OK, similarly as with quarterly precipitation. The performance of all interpolation methods was better for monthly than for quarterly precipitations. The study indicates that SMOS data can be a valuable source of auxiliary data in the cokriging and/or other multivariate methods for better estimation of the spatial distribution of precipitations in various regions of the world.
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Zhang, Yanjiang, Qing Zhen, Pengfei Li, Yongxing Cui, Junwei Xin, Yuan Yuan, Zhuhua Wu, and Xingchang Zhang. "Storage of Soil Organic Carbon and Its Spatial Variability in an Agro-Pastoral Ecotone of Northern China." Sustainability 12, no. 6 (March 13, 2020): 2259. http://dx.doi.org/10.3390/su12062259.
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Spatial distribution of soil organic carbon (SOC) is important for the development of ecosystem carbon cycle models and assessment of soil quality. In this study, a total of 732 soil samples from 122 soil profiles (0–10, 10–20, 20–40, 40–60, 60–80, and 80–100 cm) were collected by a combination of fixed-point sampling and route surveys in an agro-pastoral ecotone of northern China and the spatial variation of the SOC in the samples was analyzed through classical statistical and geostatistical approaches. The results showed that the SOC contents decreased from 4.31 g/kg in the 0–10 cm to 1.57 g/kg in the 80–100 cm soil layer. The spatial heterogeneity of the SOC exhibited moderate and strong dependence for all the soil layers owing to random and structural factors including soil texture, topography, and human activities. The spatial distributions of the SOC increased gradually from northeast to southwest in the 0–40 cm soil layers, but there was no general trend in deep soil layers and different interpolation methods resulted in the inconsistent spatial distribution of SOC. The storage of SOC was expected to be 25 Tg in the 0–100 cm soil depths for the whole area of 7692 km2. The SOC stocks estimated by two interpolation approaches were very close (25.65 vs. 25.86 Tg), but the inverse distance weighting (IDW) interpolation generated a more detailed map of SOC and with higher determination coefficient (R2); therefore, the IDW was recognized as an appropriate method to investigate the spatial variability of SOC in this region.
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Jaya, I. Gede Nyoman Mindra, Budhi Handoko, Yudhie Andriyana, Anna Chadidjah, Farah Kristiani, and Mila Antikasari. "Multivariate Bayesian Semiparametric Regression Model for Forecasting and Mapping HIV and TB Risks in West Java, Indonesia." Mathematics 11, no. 17 (August 23, 2023): 3641. http://dx.doi.org/10.3390/math11173641.
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Multivariate “Bayesian” regression via a shared component model has gained popularity in recent years, particularly in modeling and mapping the risks associated with multiple diseases. This method integrates joint outcomes, fixed effects of covariates, and random effects involving spatial and temporal components and their interactions. A shared spatial–temporal component considers correlations between the joint outcomes. Notably, due to spatial–temporal variations, certain covariates may exhibit nonlinear effects, necessitating the use of semiparametric regression models. Sometimes, choropleth maps based on regional data that is aggregated by administrative regions do not adequately depict infectious disease transmission. To counteract this, we combine the area-to-point geostatistical model with inverse distance weighted (IDW) interpolation for high-resolution mapping based on areal data. Additionally, to develop an effective and efficient early warning system for controlling disease transmission, it is crucial to forecast disease risk for a future time. Our study focuses on developing a novel multivariate Bayesian semiparametric regression model for forecasting and mapping HIV and TB risk in West Java, Indonesia, at fine-scale resolution. This novel approach combines multivariate Bayesian semiparametric regression with geostatistical interpolation, utilizing population density and the Human Development Index (HDI) as risk factors. According to an examination of annual data from 2017 to 2021, HIV and TB consistently exhibit recognizable spatial patterns, validating the suitability of multivariate modeling. The multivariate Bayesian semiparametric model indicates significant linear effects of higher population density on elevating HIV and TB risks, whereas the impact of the HDI varies over time and space. Mapping of HIV and TB risks in 2022 using isopleth maps shows a clear HIV and TB transmission pattern in West Java, Indonesia.
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Čakmak, Dragan, Jelena Beloica, Veljko Perović, Ratko Kadović, Vesna Mrvić, Jasmina Knežević, and Snežana Belanović. "Atmospheric Deposition Effects on Agricultural Soil Acidification State — Key Study: Krupanj Municipality." Archives of Environmental Protection 40, no. 2 (July 8, 2014): 137–48. http://dx.doi.org/10.2478/aep-2014-0022.
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Abstract Acidification, as a form of soil degradation is a process that leads to permanent reduction in the quality of soil as the most important natural resource. The process of soil acidification, which in the first place implies a reduction in soil pH, can be caused by natural processes, but also considerably accelerated by the anthropogenic influence of excessive S and N emissions, uncontrolled deforestation, and intensive agricultural processes. Critical loads, i.e. the upper limit of harmful depositions (primarily of S and N) which will not cause damages to the ecosystem, were determined in Europe under the auspices of the Executive Committee of the CLRTAP in 1980. These values represent the basic indicators of ecosystem stability to the process of acidification. This paper defines the status of acidification for the period up to 2100 in relation to the long term critical and target loading of soil with S and N on the territory of Krupanj municipality by applying the VSD model. The Inverse Distance Weighting (IDW) geostatistic module was used as the interpolation method. Land management, particularly in areas susceptible to acidification, needs to be focused on well-balanced agriculture and use of crops/seedlings to achieve the optimum land use and sustainable productivity for the projected 100-year period.
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Seck, Ibrahim, and Joël Van Baelen. "Geostatistical Merging of a Single-Polarized X-Band Weather Radar and a Sparse Rain Gauge Network over an Urban Catchment." Atmosphere 9, no. 12 (December 14, 2018): 496. http://dx.doi.org/10.3390/atmos9120496.
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Optimal Quantitative Precipitation Estimation (QPE) of rainfall is crucial to the accuracy of hydrological models, especially over urban catchments. Small-to-medium size towns are often equipped with sparse rain gauge networks that struggle to capture the variability in rainfall over high spatiotemporal resolutions. X-band Local Area Weather Radars (LAWRs) provide a cost-effective solution to meet this challenge. The Clermont Auvergne metropolis monitors precipitation through a network of 13 rain gauges with a temporal resolution of 5 min. 5 additional rain gauges with a 6-minute temporal resolution are available in the region, and are operated by the national weather service Météo-France. The LaMP (Laboratoire de Météorologie Physique) laboratory’s X-band single-polarized weather radar monitors precipitation as well in the region. In this study, three geostatistical interpolation techniques—Ordinary kriging (OK), which was applied to rain gauge data with a variogram inferred from radar data, conditional merging (CM), and kriging with an external drift (KED)—are evaluated and compared through cross-validation. The performance of the inverse distance weighting interpolation technique (IDW), which was applied to rain gauge data only, was investigated as well, in order to evaluate the effect of incorporating radar data on the QPE’s quality. The dataset is comprised of rainfall events that occurred during the seasons of summer 2013 and winter 2015, and is exploited at three temporal resolutions: 5, 30, and 60 min. The investigation of the interpolation techniques performances is carried out for both seasons and for the three temporal resolutions using raw radar data, radar data corrected from attenuation, and the mean field bias, successively. The superiority of the geostatistical techniques compared to the inverse distance weighting method was verified with an average relative improvement of 54% and 31% in terms of bias reduction for kriging with an external drift and conditional merging, respectively (cross-validation). KED and OK performed similarly well, while CM lagged behind in terms of point measurement QPE accuracy, but was the best method in terms of preserving the observations’ variance. The correction schemes had mixed effects on the multivariate geostatistical methods. Indeed, while the attenuation correction improved KED across the board, the mean field bias correction effects were marginal. Both radar data correction schemes resulted in a decrease of the ability of CM to preserve the observations variance, while slightly improving its point measurement QPE accuracy.
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Barrena-González, Jesús, Joaquín Francisco Lavado Contador, and Manuel Pulido Fernández. "Mapping Soil Properties at a Regional Scale: Assessing Deterministic vs. Geostatistical Interpolation Methods at Different Soil Depths." Sustainability 14, no. 16 (August 13, 2022): 10049. http://dx.doi.org/10.3390/su141610049.
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To determine which interpolation technique is the most suitable for each case study is an essential task for a correct soil mapping, particularly in studies performed at a regional scale. So, our main goal was to identify the most accurate method for mapping 12 soil variables at three different depth intervals: 0–5, 5–10 and >10 cm. For doing that, we have compared nine interpolation methods (deterministic and geostatistical), drawing soil maps of the Spanish region of Extremadura (41,635 km2 in size) from more than 400 sampling sites in total (e.g., more than 500 for pH for the depth of 0–5 cm). We used the coefficient of determination (R2), the mean error (ME) and the root mean square error (RMSE) as statistical parameters to assess the accuracy of each interpolation method. The results indicated that the most accurate method varied depending on the property and depth of study. In soil properties such as clay, EBK (Empirical Bayesian Kriging) was the most accurate for 0–5 cm layer (R2 = 0.767 and RMSE = 3.318). However, for 5–10 cm in depth, it was the IDW (Inverse Distance Weighted) method with R2 and RMSE values of 0.689 and 5.131, respectively. In other properties such as pH, the CRS (Completely Regularized Spline) method was the best for 0–5 cm in depth (R2 = 0.834 and RMSE = 0.333), while EBK was the best for predicting values below 10 cm (R2 = 0.825 and RMSE = 0.399). According to our findings, we concluded that it is necessary to choose the most accurate interpolation method for a proper soil mapping.
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ZARE-MEHRJARDI, Mohammad, Ruhollah TAGHIZADEH-MEHRJARDI, and Ali AKBARZADEH. "Evaluation of Geostatistical Techniques for Mapping Spatial Distribution of Soil PH, Salinity and Plant Cover Affected by Environmental Factors in Southern Iran." Notulae Scientia Biologicae 2, no. 4 (December 5, 2010): 92–103. http://dx.doi.org/10.15835/nsb244997.
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The study presented in this paper attempts to evaluate some interpolation techniques for mapping spatial distribution of soil pH, salinity and plant cover in Hormozgan province, Iran. The relationships among environmental factors and distribution of vegetation types were also investigated. Plot sampling was applied in the study area. Landform parameters of each plot were recorded and canopy cover percentages of each species were measured while stoniness and browsing damage were estimated. Results indicated that there was a significant difference in vegetation cover for high and low slope steepness. Also, vegetation cover was greater than other cases in the mountains with calcareous lithology. In general, there were no significant relationships among vegetation cover and soil properties such as pH, EC, and texture. Other soil properties, such as soil depth and gravel percentage were significantly affected by vegetation cover. Moreover, the geostatistical results showed that kriging and cokriging methods were better than inverse distance weighting (IDW) method for prediction of the spatial distribution of soil properties. Also, the results indicated that all the concerned soil and plant parameters were better determined by means of a cokriging method. Land elevation, which was highly correlated with studied parameters, was used as an auxiliary parameter.
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Aldegunde, José Antonio Álvarez, Adrián Fernández Sánchez, Manuel Saba, Edgar Quiñones Bolaños, and José Úbeda Palenque. "Analysis of PM2.5 and Meteorological Variables Using Enhanced Geospatial Techniques in Developing Countries: A Case Study of Cartagena de Indias City (Colombia)." Atmosphere 13, no. 4 (March 22, 2022): 506. http://dx.doi.org/10.3390/atmos13040506.
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The dispersion of air pollutants and the spatial representation of meteorological variables are subject to complex atmospheric local parameters. To reduce the impact of particulate matter (PM2.5) on human health, it is of great significance to know its concentration at high spatial resolution. In order to monitor its effects on an exposed population, geostatistical analysis offers great potential to obtain high-quality spatial representation mapping of PM2.5 and meteorological variables. The purpose of this study was to define the optimal spatial representation of PM2.5, relative humidity, temperature and wind speed in the urban district in Cartagena, Colombia. The lack of data due to the scarcity of stations called for an ad hoc methodology, which included the interpolation implementing an ordinary kriging (OK) model, which was fed by data obtained through the inverse distance weighting (IDW) model. To consider wind effects, empirical Bayesian kriging regression prediction (EBK) was implemented. The application of these interpolation methods clarified the areas across the city that exceed the recommended limits of PM2.5 concentrations (Zona Franca, Base Naval and Centro district), and described in a continuous way, on the surface, three main weather variables. Positive correlations were obtained for relative humidity (R2 of 0.47), wind speed (R2 of 0.59) and temperature (R2 of 0.64).
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Magige, Ephie A., Peng-Zhen Fan, Moses C. Wambulwa, Richard Milne, Zeng-Yuan Wu, Ya-Huang Luo, Raees Khan, et al. "Genetic Diversity and Structure of Persian Walnut (Juglans regia L.) in Pakistan: Implications for Conservation." Plants 11, no. 13 (June 22, 2022): 1652. http://dx.doi.org/10.3390/plants11131652.
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Persian (Common) walnut (Juglans regia L.) is a famous fruit tree species valued for its nutritious nuts and high-quality wood. Although walnut is widely distributed and plays an important role in the economy and culture of Pakistan, the genetic diversity and structure of its populations in the country remains poorly understood. Therefore, using 31 nuclear microsatellites, we assessed the genetic diversity and population structure of 12 walnut populations sampled across Pakistan. We also implemented the geostatistical IDW technique in ArcGIS to reveal “hotspots” of genetic diversity. Generally, the studied populations registered relatively low indices of genetic diversity (NA = 3.839, HO = 0.558, UHE = 0.580), and eight populations had positive inbreeding coefficient (FIS) values. Low among-population differentiation was indicated by AMOVA, pairwise FST and DC. STRUCTURE, PCoA and neighbor joining (NJ) analysis revealed a general lack of clear clustering in the populations except that one population in Upper Dir was clearly genetically distinct from the rest. Furthermore, the Mantel test showed no correlation between the geographic and genetic distance (r = 0.14, p = 0.22), while barrier analysis suggested three statistically significant genetic barriers. Finally, the spatial interpolation results indicated that populations in Ziarat, Kashmir, Dir, Swat, Chitral, and upper Dir had high intrapopulation genetic diversity, suggesting the need to conserve populations in those areas. The results from this study will be important for future breeding improvement and conservation of walnuts in Pakistan.
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Abdulkadir, Y. "ANALYSIS OF CLIMATE DATA USING SPATIAL TECHNIQUES TO ESTIMATE RAINFALL IN THE NORTH WEST OF NIGERIA." FUDMA JOURNAL OF SCIENCES 5, no. 4 (January 28, 2022): 174–81. http://dx.doi.org/10.33003/fjs-2021-0504-804.
Повний текст джерелаАнотація:
The study inspect the spatial variation of Rainfall in different localities in the North West of Nigeria, Rainfall data present the basic metrological role in many field of geostatistical and practice, that is why it’s one of the major climate resources that can be used as a measuring tool of climate change. The aim was to analyzed the data of one decade for thirty sample locations from (2010 – 2019) obtained from NIMET using three different spatial models and compare the models performance in order to obtained the optimal model that can be used for rainfall prediction in the study Area. The assessment of the optimal model is based on the validation methods used in the research that is the method of RMSE and R2. The supportive auxiliary variables which have been used in estimating neighboring locations are Humidity, Temperature, Pressure and Wind speed. The predicted Rainfall in the models has proved the theory of ITCZ, and the locations with a higher predicted Rainfall are in the southern part while the locations with a lower predicted Rainfall are in the northern part of the study Area in all the models, regarding the validation methods used in the research, Geographically weighted Regression (GWR) outperform Ordinary Kring (O.K) and Inverse Distance Weighting (IDW) in terms of RMSE and R2.
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Xavier Júnior, Sílvio Fernando Alves, Jader da Silva Jale, Tatijana Stosic, Carlos Antonio Costa dos Santos, and Vijay P. Singh. "Precipitation trends analysis by Mann-Kendall test: a case study of Paraíba, Brazil." Revista Brasileira de Meteorologia 35, no. 2 (June 2020): 187–96. http://dx.doi.org/10.1590/0102-7786351013.
Повний текст джерелаАнотація:
Abstract This work aimed to select semivariogram models to estimate trends in monthly precipitation in Paraiba State-Brazil using ordinary kriging. The methodology involves the application of geostatistical interpolation of precipitation records of 51 years from 69 rainfall stations across the state. Analysis of semivariograms showed that specific months had a strong spatial dependence (Index of Spatial Dependence - IDE < 25%). The trends were subjected to the following models: circular, spherical, pentaspherical, exponential, Gaussian, rational quadratic, K-Bessel and tetraspherical. The best fit models were selected by cross-validation and Error Comparison Index (ECI). Each data set (month) had a particular spatial dependence structure, which made it necessary to define specific models of semivariogram in order to enhance the adjustment of the experimental semivariogram. Besides, the monthly trend map was plotted to justify the chosen models.
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Putranto, Thomas Triadi, and Kevin Alexander. "APLIKASI GEOSPASIAL MENGGUNAKAN ARCGIS 10.3 DALAM PEMBUATAN PETA DAYA HANTAR LISTRIK DI CEKUNGAN AIRTANAH SUMOWONO." Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan 14, no. 1 (May 15, 2017): 15. http://dx.doi.org/10.14710/presipitasi.v14i1.15-23.
Повний текст джерелаАнотація:
Air tanah sebagai air bersih merupakan salah satu kebutuhan primer manusia yang dimanfaatkan dalam berbagai kepentingan manusia serta untuk air minum. Airtanah memiliki kualitas dimana salah satu parameter fisiknya adalah daya hantar listrik (DHL). Dalam suatu Cekungan Airtanah (CAT), airtanah memiliki keberagaman nilai daya hantar listrik yang dipengaruhi oleh faktor infiltrasi dan lingkungan. Nilai DHL dapat dijadikan suatu acuan mengenai kelayakan suatu airtanah sebagai air minum. Sebagai salah satu sumber yang paling diminati masyarakat, maka masyarakat juga perlu untuk mengetahui kualitas dari airtanah tersebut melalui parameter daya hantar listrik sehingga peta daya hantar listrik daerah CAT Sumowono dapat menjadi suatu informasi bagi masyarakat yang menggunakan airtanah dari CAT Sumowono tersebut. Maka dari itu perlu adanya pembuatan peta daya hantar listrik daerah CAT Sumowono agar masyarakat merasa nyaman dan aman dalam memanfaatkan airtanah. Metode interpolasi data DHL menggunakan analisis geostatistik yang terdapat pada perangkat lunak ArcGIS 10.3. Metode interpolasi yang digunakan adalah Inverse Distance Weighting (IDW), Radial Basis Functions (RBF) dan Empirical Bayesian Kriging (EBK). Dari keseluruhan data yang terinterpolasi, didapatkan dua kelas kualitas airtanah berdasarkan nilai DHL, yaitu Sangat Baik (<250 μS/cm) dan Baik (250-750 μS/cm). Metode interpolasi yang dinilai paling seimbang adalah metode RBF. Melalui peta DHL hasil interpolasi metode RBF diketahui persebaran daerah dengan kelas sangat baik pada daerah CAT Sumowono mencakup 52,8% dari luas CAT dan 47,2% masuk ke dalam kelas baik.
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Ghadimi, M., and M. A. Nezammahalleh. "CONSTRUCTION OF A CAUSEWAY BRIDGE ACROSS THE LAKE URMIA AND ITS INFLUENCE ON DRYING TREND OF THE LAKE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 211–13. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-211-2015.
Повний текст джерелаАнотація:
Construction of a causeway bridge on the Lake Urmia accelerated the drying trend of the largest hyper-saline lake of the world. The objective of the research is to investigate the differences of precipitation and river discharge before and after initiation of the construction of the bridge in 2000. The study area was the watershed of the lake. The averages of the precipitation data in the two periods before and after the project have been interpolated by IDW based on GIS Geostatistical Analyst. The two interpolated precipitation layers were used to be plugged into Student T-test equation in GIS in a spatial basis. To do this, the study area was divided to 25 regions based on drainage sub-basins. Less than 30 sample areas were randomly selected as cases from each of the regions to put into the equation. The discharge data were also compared for the two periods. The results indicated that except in some limited areas, the precipitation differences in the two periods are significant. This means that there were little changes in precipitation and river discharge in the area and consequently the drying may be caused mainly by hydrodynamic changes in the lake due to construction of the causeway. However, it can be argued that the changes in the lake’s surface area are accompanied by changes in precipitation and river discharge. The t test statistic can be applied samples based on spatial analysis.