Academic literature on the topic 'Kriging and cokriging models'
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Journal articles on the topic "Kriging and cokriging models"
Magno, Melissa, Ingrid Luffman, and Arpita Nandi. "Evaluating Spatial Regression-Informed Cokriging of Metals in Soils near Abandoned Mines in Bumpus Cove, Tennessee, USA." Geosciences 11, no. 11 (October 20, 2021): 434. http://dx.doi.org/10.3390/geosciences11110434.
Full textBaffoe-Twum, Edmund, Eric Asa, and Bright Awuku. "Estimating annual average daily traffic (AADT) data on low-volume roads with the cokriging technique and census/population data." Emerald Open Research 4 (April 22, 2022): 20. http://dx.doi.org/10.35241/emeraldopenres.14632.1.
Full textCarvalho, José Ruy Porto De, Alan Massaru Nakai, and José Eduardo B. A. Monteiro. "Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones." Revista Brasileira de Meteorologia 31, no. 2 (June 2016): 196–201. http://dx.doi.org/10.1590/0102-778631220150025.
Full textROGERS, DAVID J., and LUIGI SEDDA. "Statistical models for spatially explicit biological data." Parasitology 139, no. 14 (October 19, 2012): 1852–69. http://dx.doi.org/10.1017/s0031182012001345.
Full textQu, Mingkai, Xu Guang, Hongbo Liu, Yongcun Zhao, and Biao Huang. "Incorporating Auxiliary Data of Different Spatial Scales for Spatial Prediction of Soil Nitrogen Using Robust Residual Cokriging (RRCoK)." Agronomy 11, no. 12 (December 10, 2021): 2516. http://dx.doi.org/10.3390/agronomy11122516.
Full textJawak, S. D., and A. J. Luis. "Synergetic merging of Cartosat-1 and RAMP to generate improved digital elevation model of Schirmacher oasis, east Antarctica." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 517–24. http://dx.doi.org/10.5194/isprsarchives-xl-8-517-2014.
Full textZhang, Zebin, Martin Buisson, Pascal Ferrand, and Manuel Henner. "Integration of Second-Order Sensitivity Method and CoKriging Surrogate Model." Mathematics 9, no. 4 (February 18, 2021): 401. http://dx.doi.org/10.3390/math9040401.
Full textMa, Liang, and Chang Qing Zuo. "A Comparison of Spatial Interpolation Models for Mapping Rainfall Erosivity on China Mainland." Advanced Materials Research 518-523 (May 2012): 4489–95. http://dx.doi.org/10.4028/www.scientific.net/amr.518-523.4489.
Full textAkbari, Haghighi, Aghayi, Javadian, Tajrishy, and Kløve. "Assimilation of Satellite-Based Data for Hydrological Mapping of Precipitation and Direct Runoff Coefficient for the Lake Urmia Basin in Iran." Water 11, no. 8 (August 6, 2019): 1624. http://dx.doi.org/10.3390/w11081624.
Full textLy, 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.
Full textDissertations / Theses on the topic "Kriging and cokriging models"
Adu, Agyemang Adela Beauty. "Vulnerability Assessment of Groundwater to NO3 Contamination Using GIS, DRASTIC Model and Geostatistical Analysis." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3264.
Full textYATES, SCOTT RAYMOND. "GEOSTATISTICAL METHODS FOR ESTIMATING SOIL PROPERTIES (KRIGING, COKRIGING, DISJUNCTIVE)." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/187990.
Full textLong, Andrew Edmund. "Cokriging, kernels, and the SVD: Toward better geostatistical analysis." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186892.
Full textJohnson, Crystal. "Using Kriging, Cokriging, and GIS to Visualize Fe and Mn in Groundwater." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etd/2498.
Full textHemmati, Sahar. "Steady-State Co-Kriging Models." Thesis, West Virginia University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10614907.
Full textIn deterministic computer experiments, a computer code can often be run at different levels of complexity/fidelity and a hierarchy of levels of code can be obtained. The higher the fidelity and hence the computational cost, the more accurate output data can be obtained. Methods based on the co-kriging methodology Cressie (2015) for predicting the output of a high-fidelity computer code by combining data generated to varying levels of fidelity have become popular over the last two decades. For instance, Kennedy and O’Hagan (2000) first propose to build a metamodel for multi-level computer codes by using an auto-regressive model structure. Forrester et al. (2007) provide details on estimation of the model parameters and further investigate the use of co-kriging for multi-fidelity optimization based on the efficient global optimization algorithm Jones et al. (1998). Qian and Wu (2008) propose a Bayesian hierarchical modeling approach for combining low-accuracy and high-accuracy experiments. More recently, Gratiet and Cannamela (2015) propose sequential design strategies using fast cross-validation techniques for multi-fidelity computer codes.
This research intends to extend the co-kriging metamodeling methodology to study steady-state simulation experiments. First, the mathematical structure of co-kriging is extended to take into account heterogeneous simulation output variances. Next, efficient steady-state simulation experimental designs are investigated for co-kriging to achieve a high prediction accuracy for estimation of steady-state parameters. Specifically, designs consisting of replicated longer simulation runs at a few design points and replicated shorter simulation runs at a larger set of design points will be considered. Also, design with no replicated simulation runs at long simulation is studied, along with different methods for calculating the output variance in absence of replicated outputs.
Stochastic co-kriging (SCK) method is applied to an M/M/1, as well as an M/M/5 queueing system. In both examples, the prediction performance of the SCK model is promising. It is also shown that the SCK method provides better response surfaces compared to the SK method.
Watanabe, Jorge. "Métodos geoestatísticos de co-estimativas: estudo do efeito da correlação entre variáveis na precisão dos resultados." Universidade de São Paulo, 2008. http://www.teses.usp.br/teses/disponiveis/44/44137/tde-14082008-165227/.
Full textThis master dissertation presents the results of a survey into co-estimation methods commonly used in geostatistics. These methods are ordinary cokriging, collocated cokriging and kriging with an external drift. Besides that ordinary kriging was considered just to illustrate how it does work when the primary variable is poorly sampled. As we know co-estimation methods depend on a secondary variable sampled over the estimation domain. Moreover, this secondary variable should present linear correlation with the main variable or primary variable. Usually the primary variable is poorly sampled whereas the secondary variable is known over the estimation domain. For instance in oil exploration the primary variable is porosity as measured on rock samples gathered from drill holes and the secondary variable is seismic amplitude derived from processing seismic reflection data. It is important to mention that primary and secondary variables must present some degree of correlation. However, we do not know how they work depending on the correlation coefficient. That is the question. Thus, we have tested co-estimation methods for several data sets presenting different degrees of correlation. Actually, these data sets were generated in computer based on some data transform algorithms. Five correlation values have been considered in this study: 0.993; 0.870; 0.752; 0.588 and 0.461. Collocated simple cokriging was the best method among all tested. This method has an internal filter applied to compute the weight for the secondary variable, which in its turn depends on the correlation coefficient. In fact, the greater the correlation coefficient the greater the weight of secondary variable is. Then it means this method works even when the correlation coefficient between primary and secondary variables is low. This is the most impressive result that came out from this research.
Araújo, Cristina da Paixão. "Uso de informação secundária imprecisa e inacurada no planejamento de curto prazo." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/127891.
Full textDecisions starting at mineral exploration through mining are based on grade block models obtained from samples. To decrease the uncertainty in the estimates, the short term mining planning requires additional sampling to ensure accurate and precise predictions. As more samples are made available, there is trend towards more reliable estimates. In the exploration stage, usually, sampling is performed by diamond drill holes (DDH), which are expensive but produces accurate and precise samples. In this stage there are few data with high quality. In the production stage, sampling is obtained by other techniques due to the high costs of DDHs. In general, these samples have low quality and are not controlled by QA / QC protocols. This study evaluates the impact of using imprecise data in short-term mineplanning. For this, it was analyzed two different data sets. The first case used the exhaustive Walker Lake dataset as the source to obtain the true and sampled grades. Initially, samples were obtained from the exhaustive dataset at regularly spaced grids at 20 × 20 m and 5 × 5 meters. A relative error (imprecision) of ± 25% and a 10% bias were added to the data spaced at 5 × 5 m (short-term geological data) in different scenarios. The second study is in a gold mine with two different types of data obtained from diamond drilling holes (DDH_Hard data) and reverse circulation (RC_Soft data).To combine these different types of data, two methodologies were investigated: cokriging and ordinary kriging. Both types of data were used to estimate a block model using the two methodologies. The grade tonnage curves and swath plots were used to compare the results against the true block grades at the same block support. In addition, the block misclassification was evaluated. In the Walker Lake the results show that standardized ordinary cokriging is a better methodology for imprecise and biased data and produces estimates closer to the true grade block distribution, reducing block misclassification. For the data set at the underground mine gold, the samples had moderate correlation and short spatial continuity for small distances at this deposit. In this situation, the estimates using ordinary kriging with hard and soft data (standardized and re-escaled) produced better results with less bias and better blocks classification of ore and waste.
Wang, Xiang. "Two kriging models, and the expanded readsold package." Ohio University / OhioLINK, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1183382153.
Full textMuré, Joseph. "Objective Bayesian analysis of Kriging models with anisotropic correlation kernel." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC069/document.
Full textA recurring problem in surrogate modelling is the scarcity of available data which hinders efforts to estimate model parameters. The Bayesian paradigm offers an elegant way to circumvent the problem by describing knowledge of the parameters by a posterior probability distribution instead of a pointwise estimate. However, it involves defining a prior distribution on the parameter. In the absence of expert opinion, finding an adequate prior can be a trying exercise. The Objective Bayesian school proposes default priors for such can be a trying exercise. The Objective Bayesian school proposes default priors for such situations, like the Berger-Bernardo reference prior. Such a prior was derived by Berger, De Oliveira and Sansó [2001] for the Kriging surrogate model with isotropic covariance kernel. Directly extending it to anisotropic kernels poses theoretical as well as practical problems because the reference prior framework requires ordering the parameters. Any ordering would in this case be arbitrary. Instead, we propose an Objective Bayesian solution for Kriging models with anisotropic covariance kernels based on conditional reference posterior distributions. This solution is made possible by a theory of compromise between incompatible conditional distributions. The work is then shown to be compatible with Trans-Gaussian Kriging. It is applied to an industrial case with nonstationary data in order to derive Probability Of defect Detection (POD) by non-destructive tests in steam generator tubes of nuclear power plants
Asritha, Kotha Sri Lakshmi Kamakshi. "Comparing Random forest and Kriging Methods for Surrogate Modeling." Thesis, Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20230.
Full textBooks on the topic "Kriging and cokriging models"
Design Optimization with Kriging Models (WBBM Report Series 47). Delft Univ Pr, 2000.
Find full textNational Aeronautics and Space Administration (NASA) Staff. Comparison of Response Surface and Kriging Models in the Multidisciplinary Design of an Aerospike Nozzle. Independently Published, 2018.
Find full textComparison of response surface and kriging models in the multidisciplinary design of an aerospike nozzle. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1998.
Find full textSheehan, Daniel Dean. Interpolating a regular grid of elevations from random points using three algorithms: Kriging, splines, and polynomial surfaces. 1987.
Find full textBook chapters on the topic "Kriging and cokriging models"
Christensen, Ronald. "Linear Models for Spatial Data: Kriging." In Springer Texts in Statistics, 269–311. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3847-6_6.
Full textChristensen, Ronald. "Linear Models for Spatial Data: Kriging." In Springer Texts in Statistics, 262–99. New York, NY: Springer New York, 1991. http://dx.doi.org/10.1007/978-1-4757-4103-2_6.
Full textChristensen, Ronald. "Linear Models for Spatial Data: Kriging." In Springer Texts in Statistics, 321–55. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29164-8_8.
Full textWackernagel, Hans. "Kriging with Discrete Point-Bloc Models." In Multivariate Geostatistics, 273–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05294-5_36.
Full textSchafmeister, Maria-Th. "Parameter Estimation for Groundwater Models by Indicator Kriging." In geoENV I — Geostatistics for Environmental Applications, 165–76. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-017-1675-8_14.
Full textConde, R. P., and J. K. Yamamoto. "Evaluation of Kriging and Cokriging for Asbestos Ore Reserve Estimation at the Cana Brava Mine, Goiás, Brazil." In Geostatistics Rio 2000, 191–203. Dordrecht: Springer Netherlands, 2002. http://dx.doi.org/10.1007/978-94-017-1701-4_14.
Full textBernardes, T., I. Gontijo, H. Andrade, T. G. C. Vieira, and H. M. R. Alves. "Digital Terrain Models Derived from SRTM Data and Kriging." In Lecture Notes in Geoinformation and Cartography, 673–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36998-1_51.
Full textHardtke, William, and Celeste Wilson. "Fixing Panel Artifacts in Localized Indicator Kriging (LIK) Block Models." In Geostatistics Valencia 2016, 213–23. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-46819-8_14.
Full textMorató, A., and S. Sriramula. "Reliability analysis of offshore wind turbine support structures using Kriging models." In Risk, Reliability and Safety: Innovating Theory and Practice, 1425–31. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315374987-214.
Full textPereira, Paulo Elias C., Daniel R. Gonçalves, Stanley W. F. Rezende, Jose dos Reis V. Moura, and Roberto M. Finzi. "On Kriging Techniques & Impedance-based SHM as Applied to Damage Detection in 2D Structures." In Uncertainty Modeling: Fundamental Concepts and Models, 427–58. Brasilia, DF, Brazil: Biblioteca Central da Universidade de Brasilia, 2022. http://dx.doi.org/10.4322/978-65-86503-88-3.c13.
Full textConference papers on the topic "Kriging and cokriging models"
Nagawkar, Jethro, and Leifur Leifsson. "Applications of Polynomial Chaos-Based Cokriging to Simulation-Based Analysis and Design Under Uncertainty." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22369.
Full textRumpfkeil, Markus, Wataru Yamazaki, and Mavriplis Dimitri. "A Dynamic Sampling Method for Kriging and Cokriging Surrogate Models." In 49th AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2011. http://dx.doi.org/10.2514/6.2011-883.
Full textKontoudis, George P., and Daniel J. Stilwell. "A Comparison of Kriging and Cokriging for Estimation of Underwater Acoustic Communication Performance." In WUWNET'19: International Conference on Underwater Networks & Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3366486.3366515.
Full textMartin, Jay. "Robust Kriging Models." In 51st AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
18th AIAA/ASME/AHS Adaptive Structures Conference
12th. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-2854.
Leszczynska, Natalia, Selvakumar Ulaganathan, Adam Lamecki, Tom Dhaene, and Michal Mrozowski. "Kriging models for microwave filters." In 2016 IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO). IEEE, 2016. http://dx.doi.org/10.1109/nemo.2016.7561660.
Full textMartin, Jay D., and Timothy W. Simpson. "On the Use of Kriging Models to Approximate Deterministic Computer Models." In ASME 2004 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2004. http://dx.doi.org/10.1115/detc2004-57300.
Full textMartin, Jay D., and Timothy W. Simpson. "On Using Kriging Models as Probabilistic Models in Design." In SAE 2004 World Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2004. http://dx.doi.org/10.4271/2004-01-0430.
Full textMartin, Jay D., and Timothy W. Simpson. "A Study on the Use of Kriging Models to Approximate Deterministic Computer Models." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dac-48762.
Full textMartin, Jay D. "On Using Kriging Models for Complex Design." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-48579.
Full textKrishnamurty, Sundar, and Gregory Wilmes. "Preference-Based Updating of Kriging Surrogate Models." In 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2004. http://dx.doi.org/10.2514/6.2004-4484.
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