Relevant bibliographies by topics / LANDSLIP SUSCEPTIBILITY MAPPING

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Academic literature on the topic 'LANDSLIP SUSCEPTIBILITY MAPPING'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "LANDSLIP SUSCEPTIBILITY MAPPING"

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Mamun, Al, Hyun-Su Park, and Dong-Ho JANG. "공간예측모형에 기반한 산사태 취약성 지도 작성과 품질 평가." JOURNAL OF THE GEOMORPHOLOGICAL ASSOCIATION OF KOREA 26, no. 3 (September 30, 2019): 53–67. http://dx.doi.org/10.16968/jkga.26.3.53.

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Kritikos, Theodosios R. H., and Timohty R. H. Davies. "GIS-based Multi-Criteria Decision Analysis for landslide susceptibility mapping at northern Evia, Greece." Zeitschrift der Deutschen Gesellschaft für Geowissenschaften 162, no. 4 (December 1, 2011): 421–34. http://dx.doi.org/10.1127/1860-1804/2011/0162-0421.

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Chawla, Amit, Sowmiya Chawla, Srinivas Pasupuleti, A. C. S. Rao, Kripamoy Sarkar, and Rajesh Dwivedi. "Landslide Susceptibility Mapping in Darjeeling Himalayas, India." Advances in Civil Engineering 2018 (September 16, 2018): 1–17. http://dx.doi.org/10.1155/2018/6416492.

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Landslide susceptibility map aids decision makers and planners for the prevention and mitigation of landslide hazard. This study presents a methodology for the generation of landslide susceptibility mapping using remote sensing data and Geographic Information System technique for the part of the Darjeeling district, Eastern Himalaya, in India. Topographic, earthquake, and remote sensing data and published geology, soil, and rainfall maps were collected and processed using Geographic Information System. Landslide influencing factors in the study area are drainage, lineament, slope, rainfall, earthquake, lithology, land use/land cover, fault, valley, soil, relief, and aspect. These factors were evaluated for the generation of thematic data layers. Numerical weight and rating for each factor was assigned using the overlay analysis method for the generation of landslide susceptibility map in the Geographic Information System environment. The resulting landslide susceptibility zonation map demarcated the study area into four different susceptibility classes: very high, high, moderate, and low. Particle Swarm Optimization-Support Vector Machine technique was used for the prediction and classification of landslide susceptibility classes, and Genetic Programming method was used to generate models and to predict landslide susceptibility classes in conjunction with Geographic Information System output, respectively. Genetic Programming and Particle Swarm Optimization-Support Vector Machine have performed well with respect to overall prediction accuracy and validated the landslide susceptibility model generated in the Geographic Information System environment. The efficiency of the landslide susceptibility zonation map was also confirmed by correlating the landslide frequency between different susceptible classes.
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Lin, Zian, Qiuguang Chen, Weiping Lu, Yuanfa Ji, Weibin Liang, and Xiyan Sun. "Landslide Susceptibility Mapping Based on Information-GRUResNet Model in the Changzhou Town, China." Forests 14, no. 3 (March 2, 2023): 499. http://dx.doi.org/10.3390/f14030499.

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Landslide susceptibility mapping is the basis of regional landslide risk assessment and prevention. In recent years, deep learning models have been applied in landslide susceptibility mapping, but some problems remain, such as gradient disappearance, explosion, and degradation. Additionally, the potential nonlinear temporal and spatial characteristics between landslides and environmental factors may not be captured, and nonlandslide points may be randomly selected in the susceptibility mapping process. To overcome these shortcomings, in this paper, an information-gate recurrent unit residual network (Information-GRUResNet) model is proposed to produce a landslide susceptibility map by combining existing landslide records and environmental factor data. The model uses the information theory method to produce the initial landslide susceptibility map. Then, representative grid units and landslide points are selected as input variables of the GRUResNet model, from which nonlinear temporal and spatial characteristics are extracted to produce a landslide susceptibility map. Changzhou town in Wuzhou, China, is selected as a case study, and it is verified that the Information-GRUResNet model can accurately produce a landslide susceptibility map for the selected area. Finally, the Information-GRUResNet model is compared with GRU, RF, and LR models. The experimental results show that the Information-GRUResNet model is more accurate than the other three models.
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Bathrellos, G. D., D. P. Kalivas, and H. D. Skilodimou. "GIS-based landslide susceptibility mapping models applied to natural and urban planning in Trikala, Central Greece." Estudios Geológicos 65, no. 1 (December 9, 2008): 49–65. http://dx.doi.org/10.3989/egeol.08642.036.

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Sekarlangit, Nadia, Teuku Faisal Fathani, and Wahyu Wilopo. "Landslide Susceptibility Mapping of Menoreh Mountain Using Logistic Regression." Journal of Applied Geology 7, no. 1 (June 28, 2022): 51. http://dx.doi.org/10.22146/jag.72067.

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Menoreh mountain is one of the priority areas developed for tourism and to support sustainable development, it must pay attention to disaster aspects, one of which is landslides. The map published by Center for Volcanology and Geological Hazard Mitigation of Indonesia (PVMBG) has a regional scale, so it is necessary to have a more detailed landslide susceptibility map in the Menoreh Mountains. Identification and evaluation of the landslide conditioning factor were done using logistic regression so that the zonation of the probability of landslide susceptibility can be made. The data was used from field observation conducted at 372 locations including 129 locations where landslides occurred and from a local disaster management agency (BPBD) of 200 landslide locations. Significant landslide conditioning factors include slope, lithology, distance to lineaments, distance to river, and distance to road. The research area is divided into three susceptibility zones classified into low landslide susceptibility zone (0-0.33) covering 39.82%, moderate landslide susceptibility zone (0.34-0.66) covering 25.86%, and high landslide susceptibility zone (0.67-1.00) covering 34.31% of the whole area. Analysis using the logistic regression method has a model prediction accuracy rate of 90.5%, which means that it can predict landslide occurrence in the Menoreh Mountains accurately.
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Bornaetxea, Txomin, Mauro Rossi, Ivan Marchesini, and Massimiliano Alvioli. "Effective surveyed area and its role in statistical landslide susceptibility assessments." Natural Hazards and Earth System Sciences 18, no. 9 (September 14, 2018): 2455–69. http://dx.doi.org/10.5194/nhess-18-2455-2018.

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Abstract. Geomorphological field mapping is a conventional method used to prepare landslide inventories. The approach is typically hampered by the accessibility and visibility, during field campaigns for landslide mapping, of the different portions of the study area. Statistical significance of landslide susceptibility maps can be significantly reduced if the classification algorithm is trained in unsurveyed regions of the study area, for which landslide absence is typically assumed, while ignorance about landslide presence should actually be acknowledged. We compare different landslide susceptibility zonations obtained by training the classification model either in the entire study area or in the only portion of the area that was actually surveyed, which we name effective surveyed area. The latter was delineated by an automatic procedure specifically devised for the purpose, which uses information gathered during surveys, along with landslide locations. The method was tested in Gipuzkoa Province (Basque Country), north of the Iberian Peninsula, where digital thematic maps were available and a landslide survey was performed. We prepared the landslide susceptibility maps and the associated uncertainty within a logistic regression model, using both slope units and regular grid cells as the reference mapping unit. Results indicate that the use of effective surveyed area for landslide susceptibility zonation is a valid approach that minimises the limitations stemming from unsurveyed regions at landslide mapping time. Use of slope units as mapping units, instead of grid cells, mitigates the uncertainties introduced by training the automatic classifier within the entire study area. Our method pertains to data preparation and, as such, the relevance of our conclusions is not limited to the logistic regression but are valid for virtually all the existing multivariate landslide susceptibility models.
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Habumugisha, Jules Maurice, Ningsheng Chen, Mahfuzur Rahman, Md Monirul Islam, Hilal Ahmad, Ahmed Elbeltagi, Gitika Sharma, Sharmina Naznin Liza, and Ashraf Dewan. "Landslide Susceptibility Mapping with Deep Learning Algorithms." Sustainability 14, no. 3 (February 2, 2022): 1734. http://dx.doi.org/10.3390/su14031734.

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Among natural hazards, landslides are devastating in China. However, little is known regarding potential landslide-prone areas in Maoxian County. The goal of this study was to apply four deep learning algorithms, the convolutional neural network (CNN), deep neural network (DNN), long short-term memory (LSTM) networks, and recurrent neural network (RNN) in evaluating the possibility of landslides throughout Maoxian County, Sichuan, China. A total of 1290 landslide records was developed using historical records, field observations, and remote sensing techniques. The landslide susceptibility maps showed that most susceptible areas were along the Minjiang River and in some parts of the southeastern portion of the study area. Slope, rainfall, and distance to faults were the most influential factors affecting landslide occurrence. Results revealed that proportion of landslide susceptible areas in Maoxian County was as follows: identified landslides (13.65–23.71%) and non-landslides (76.29–86.35%). The resultant maps were tested against known landslide locations using the area under the curve (AUC). This study indicated that the DNN algorithm performed better than LSTM, CNN, and RNN in identifying landslides in Maoxian County, with AUC values (for prediction accuracy) of 87.30%, 86.50%, 85.60%, and 82.90%, respectively. The results of this study are useful for future landslide risk reduction along with devising sustainable land use planning in the study area.
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Thanh, Dang Quang, Duy Huu Nguyen, Indra Prakash, Abolfazl Jaafari, Viet Tien Nguyen, Tran Van Phong, and Binh Thai Pham. "GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong province, Vietnam." VIETNAM JOURNAL OF EARTH SCIENCES 42, no. 1 (January 15, 2020): 55–66. http://dx.doi.org/10.15625/0866-7187/42/1/14758.

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Landslide susceptibility mapping of the city of Da Lat, which is located in the landslide prone area of Lam Dong province of Central Vietnam region, was carried out using GIS based frequency ratio (FR) method. There are number of methods available but FR method is simple and widely used method for landslide susceptibility mapping. In the present study, eight topographical and geo-environmental landslide-conditioning factors were used including slope, elevation, land use, weathering crust, soil, lithology, distance to geology features, and stream density in conjunction with 70 past landslide locations. The results show that 6.27% of the area is in the very low susceptibility area, 21.03% in the low susceptibility area, 27.09% in the moderate susceptibility area and 27.41% of the area is in the high susceptibility zone and 18.21% in the very high susceptibility zone. The landslide susceptibility map produced in this study helps to assist decision makers in proper land use management and planning.
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Yu, Chenglong, and Jianping Chen. "Application of a GIS-Based Slope Unit Method for Landslide Susceptibility Mapping in Helong City: Comparative Assessment of ICM, AHP, and RF Model." Symmetry 12, no. 11 (November 9, 2020): 1848. http://dx.doi.org/10.3390/sym12111848.

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Landslides are one of the most extensive geological disasters in the world. The objective of this study was to assess the performances of different landslide susceptibility models information content method (ICM), analytical hierarchy process (AHP), and random forest (RF) model) and mapping unit (slope unit and grid unit) for landslide susceptibility mapping in the Helong city, Jilin province, northeastern China. First, a total of 159 landslides were mapped in the study area based on a geological hazard survey (1:50,000) of Helong city. Then, the slope units of the study area were divided by using the curvature watershed method. Next, eight influencing factors, namely, lithology, slope angle, slope aspect, rainfall, land use, seismic intensity, distance to river, and distance to fault, were selected to map the landslide susceptibility based on geological data, field survey, and landslide information. Afterward, landslide susceptibility modeling of landslide inventory data is performed for extracting and learning the symmetry latent in data patterns and relationships by three landslide susceptibility models and utilizing it to predict landslide susceptibility. Finally, the receiver operating characteristic (ROC) curve was used to compare the landslide susceptibility models. In addition, results based on grid units were calculated for comparison. The AUC (the area under the curve) result for ICM, AHP, and RF model was 87.1%, 80.5%, and 94.6% for slope units, and 83.4%, 70.9%, and 91.3% for grid units, respectively. Based on the overall assessments, the SU-RF model was the most suitable model for landslide susceptibility mapping. Consequently, these methods can be very useful for landslide hazard mitigation strategies.
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Dissertations / Theses on the topic "LANDSLIP SUSCEPTIBILITY MAPPING"

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Tyoda, Zipho. "Landslide susceptibility mapping : remote sensing and GIS approach." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/79856.

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Thesis (MSc)--Stellenbosch University, 2013.
Landslide susceptibility maps are important for development planning and disaster management. The current synthesis of landslide susceptibility maps largely applies GIS and remote sensing techniques. One of the most critical stages on landslide susceptibility mapping is the selection of landslide causative factors and weighting of the selected causative factors, in accordance to their influence to slope instability. GIS is ideal when deriving static factors i.e. slope and aspect and most importantly in the synthesis of landslide susceptibility maps. The integration of landslide causative thematic maps requires the selection of the weighting method; in order to weight the causative thematic maps in accordance to their influence to slope instability. Landslide susceptibility mapping is based on the assumption that future landslides will occur under similar circumstances as historic landslides. The weight of evidence method is ideal for landslide susceptibility mapping, as it calculates the weights of the causative thematic maps using known landslides points. This method was applied in an area within the Western Cape province of South Africa, the area is known to be highly susceptible to landslide occurrences. A prediction rate of 80.37% was achieved. The map combination approach was also applied and achieved a prediction rate of 50.98%. Satellite remote sensing techniques can be used to derive the thematic information needed to synthesize landslide susceptibility maps and to monitor the variable parameters influencing landslide susceptibility. Satellite remote sensing techniques can contribute to landslide investigation at three distinct phases namely: (1) detection and classification of landslides (2) monitoring landslide movement and identification of conditions leading up to an event (3) analysis and prediction of slope failures. Various sources of remote sensing data can contribute to these phases. Although the detection and classification of landslides through the remote sensing techniques is important to define landslide controlling parameters, the ideal is to use remote sensing data for monitoring of areas susceptible to landslide occurrence in an effort to provide an early warning. In this regard, optical remote sensing data was used successfully to monitor the variable conditions (vegetation health and productivity) that make an area susceptible to landslide occurrence.
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Yilmaz, Cagatay. "Gis-based Landslide Susceptibility Mapping In Devrek (zonguldak &amp." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12608805/index.pdf.

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The purpose of this study is to evaluate and to compare the results of bivariate statistical analysis conducted with three different data sets in Geographical Information Systems (GIS) based landslide susceptibility mapping applied to the Devrek region. The data sets are created from the seed cells of crowns and flanks, only crowns, and only flanks of the landslides by using 10 different parameters of the study area. To increase the data dependency of the analysis, all parameter maps are classified into equal frequency classes based directly on the percentile divisions of each seed cells data set. The resultant maps of the landslide susceptibility analysis indicate that all data sets produce acceptable results. In each seed cell data set analysis, elevation, lithology, slope, aspect and drainage density parameters are found to be the most contributing factors in landslide occurrences. The results of the three data sets are compared by Seed Cell Area Index (SCAI). This comparison shows that the crowns data set produces the most accurate and successful landslide susceptibility map of the study area.
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Ha, Le Thi Chau. "Remote sensing data integration for landslide susceptibility mapping in Vietnam." Thesis, University of Newcastle Upon Tyne, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493229.

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Barik, Muhammad G. "Landslide susceptibility mapping to inform landuse management decisions in an altered climate." Pullman, Wash. : Washington State University, 2010. http://www.dissertations.wsu.edu/Thesis/Spring2010/m_barik_042310.pdf.

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Thesis (M.S. in civil engineering)--Washington State University, May 2010.
Title from PDF title page (viewed on June 23, 2010). "Department of Civil and Environmental Engineering." Includes bibliographical references (p. 51-56).
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Growney, Lawrence P. "Landslide Inventory and Susceptibility Mapping of the Upper Canyon Creek Basin, Cascade Range, Skamania County, Washington." PDXScholar, 1994. https://pdxscholar.library.pdx.edu/open_access_etds/5016.

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Contact relations, and bedrock and overburden characteristics for approximately 8100 ha of the upper Canyon Creek basin, Skamania County, Washington, have been assessed in order to determine the causes and extent of failures and to assign slope failure susceptibilities to the area. The study area is located in the western Cascade Range on land administered by the Gifford Pinchot National Forest. Clear-cutting over the past 30 years has impacted between 50% to 80% of the study area. The total surface area occupied by failure deposits (198.6 ha) is less than 2.5% of the study area. Failures occur by one of seven processes, in decreasing order of abundance: rockfall (53.6%), rock avalanche (25.3%), slumps (15.6%), streambank failures (3.4%), soil and debris slips (1%), snow avalanches (debris falls) (1%), and translational slides (0.1%). Integrity of the bedrock is primarily influenced by jointing characteristics, in particular: dilation, orientation and continuity. Groundwater is an important constituent in the failure of fragmental igneous bedrock, but has very little impact in inducing failure in compact igneous bedrock. Areas underlain by fragmental igneous bedrock have a proportionally greater number of translational and rotational failures. With increasing compact igneous bedrock content, small volume rockfall failures become more predominant. Sixteen to twenty percent of the roadbed surfaces in the study area are experiencing some type of failure. Up to 99 percent of roadbed failures are confined to the roadfill prism. Failure due to degradation of the subgrade is rarely obseived. Arcuate and sliver-like cracks, offsets, sinkholes, concentrations of potholes, broad slumps and chute formation in the roadfill are indicators of failure. Ditches without culverts, or with poorly placed, damaged or leaking culverts, result in oversaturation and piping within the fill which may lead to failure of the road. The potential for slope failure is assigned a rating of low, moderate or high. These ratings are based on a qualitative assessment of the impact of various factors on the factor of safety, through their ability to reduce the cohesion and friction of affected rock and soil masses. Low susceptibility areas cover approximately 10 percent of the area (810 ha). Slopes are less than 3.5 degrees. Nearly 70 percent of the study area can be classified as moderately susceptable (5670 ha). Slopes in these areas range up to the natural angle of repose. The high susceptibility category covers areas with near vertical slopes, continuous rockfall, previous failures or strong indications of potential failure. These areas cover about 20 percent of the basin ( 1620 ha) and include areas of actual failure and adjacent areas which have not failed but possess similar bedrock, cultural and groundwater characteristics.
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Festa, Davide. "Debris flow susceptibility mapping for initiation areas at medium scale: a case study in Western Norway." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18141/.

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In recent years, rapid mass movements such as debris flow and debris avalanches resulted in a significant impact on Norwegian society and economy. The need for dispelling the uncertainty inherent in landslide risk assessment has encouraged the development of hazard and susceptibility maps. Different statistically-based modelling methods, in combination with geographic information systems (GIS), have been extensively used to ascertain landslide susceptibility in quantitative terms. This thesis proposes a bivariate statistical method (Weights of Evidence) for assessing the spatial proneness of debris flows within Førde and Jølster municipalities (Western Norway), where emphasis is put on the critical conditions of initiation. Since no feasible landslide database could be exploited for susceptibility mapping at medium scale, this thesis addressed the realisation of a new inventory. By coupling pre-existing data from remote sensing and field observations, circa 1100 debris flow initiation areas were outlined and differentiated in four categories with geomorphological repeatable features. Simple topography-based parameters such as slope, upslope contributing area, curvature and roughness were used to find significant statistical differences between the initiation areatypes. Moreover, they were employed together with other thematic maps as informative layers for landslide modelling. In order to test the model fitting performance, the ROC curves method is used in this thesis. The evaluation of different discretization schemes and combinations of the above-mentioned variables led to individuate models with different performances in terms of success rates. The best model is obtained by using only a combination of slope, flow accumulation and elevation (82% true positive rate), while the manual adjustment of the classification scheme did not lead to significant improvements.
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Erener, Arzu. "An Approach For Landslide Risk Assesment By Using Geographic Information Systems (gis) And Remote Sensing (rs)." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611314/index.pdf.

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This study aims to develop a Geographic Information Systems (GIS) and Remote Sensing (RS) Based systematic quantitative landslide risk assessment methodology for regional and local scales. Each component of risk, i.e., hazard assessment, vulnerability, and consequence analysis, is quantitatively assessed for both scales. The developed landslide risk assessment methodology is tested at Kumluca watershed, which covers an area of 330 km2, in Bartin province of the Western Black Sea Region, Turkey. GIS and RS techniques are used to create landslide factor maps, to obtain susceptibility maps, hazard maps, elements at risk and risk maps, and additionally to compare the obtained maps. In this study, the effect of mapping unit and mapping method upon susceptibility mapping method, and as a result the effect upon risk map, is evaluated. Susceptibility maps are obtained by using two different mapping units, namely slope unit-based and grid-based mapping units. When analyzing the effect of susceptibility mapping method, this study attempts to extend Logistic Regression (LR) and Artificial Neural Network (ANN) by implementing Geographically-Weighted Logistic Regression (GWR) and spatial regression (SR) techniques for landslide susceptibility assessment. In addition to spatial probability of occurrence of damaging events, landslide hazard calculation requires the determination of the temporal probability. Precipitation triggers the majority of landslides in the study region. The critical rainfall thresholds were estimated by using daily and antecedent rainfalls and landslide occurrence dates based on three different approaches: Time Series, Gumble Distribution and Intensity Duration Curves. Different procedures are adopted to obtain the element at risk values and vulnerability values for local and regional scale analyses. For regional scale analysis, the elements at risk were obtained from existing digital cadastral databases and vulnerabilities are obtained by adopting some generalization approaches. On the other hand, on local scale the elements at risk are obtained by high resolution remote sensing images by the developed algorithms in an automatic way. It is found that risk maps are more similar for slope unit-based mapping unit than grid&ndash
based mapping unit.
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Bi, Renneng [Verfasser], and Joachim [Akademischer Betreuer] Rohn. "Geotechnical mapping and landslide susceptibility analysis in Badong county (Three Gorges Region / China) / Renneng Bi. Gutachter: Joachim Rohn." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2015. http://d-nb.info/1075839416/34.

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Mickelson, Katherine A. "LiDAR-Based Landslide Inventory and Susceptibility Mapping, and Differential LiDAR Analysis for the Panther Creek Watershed, Coast Range, Oregon." PDXScholar, 2011. https://pdxscholar.library.pdx.edu/open_access_etds/253.

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LiDAR (Light Detection and Ranging) elevation data were collected in the Panther Creek Watershed, Yamhill County, Oregon in September and December, 2007, March, 2009 and March, 2010. LiDAR derived images from the March, 2009 dataset were used to map pre-historic, historic, and active landslides. Each mapped landslide was characterized as to type of movement, head scarp height, slope, failure depth, relative age, and direction. A total of 153 landslides were mapped and 81% were field checked in the study area. The majority of the landslide deposits (127 landslides) appear to have had movement in the past 150 years. Failures occur on slopes with a mean estimated pre-failure slope of 27° ± 8°. Depth to failure surfaces for shallow-seated landslides ranged from 0.75 m to 4.3 m, with an average of 2.9 m ± 0.8 m, and depth to failure surfaces for deep-seated landslides ranged from 5 m to 75m, with an average of 18 m ± 14 m. Earth flows are the most common slope process with 110 failures, comprising nearly three quarters (71%) of all mapped deposits. Elevation changes from two of the successive LiDAR data sets (December, 2007 and March, 2009) were examined to locate active landslides that occurred between the collections of the LiDAR imagery. The LiDAR-derived DEMs were subtracted from each other resulting in a differential dataset to examine changes in ground elevation. Areas with significant elevation changes were identified as potentially active landslides. Twenty-six landslides are considered active based upon differential LiDAR and field observations. Different models are used to estimate landslide susceptibility based upon landslide failure depth. Shallow-seated landslides are defined in this study as having a failure depth equal to less than 4.6 m (15 ft). Results of the shallow-seated susceptibility map show that the high susceptibility zone covers 35% and the moderate susceptibility zone covers 49% of the study area. Due to the high number of deep-seated landslides (58 landslides), a deep-seated susceptibility map was also created. Results of the deep-seated susceptibility map show that the high susceptibility zone covers 38% of the study area and the moderate susceptibility zone covers 43%. The results of this study include a detailed landslide inventory including pre-historic, historic, and active landslides and a set of susceptibility maps identifying areas of potential future landslides.
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Palau, Berastegui Rosa Maria. "Landslide and debris flow warning at regional scale. A real-time system using susceptibility mapping, radar rainfall and hydrometeorological thresholds." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/672681.

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Rainfall triggered shallow slides and debris flows constitute a significant hazard that causes substantial economic losses and fatalities worldwide. Regional-scale risk mitigation for these processes is challenging. Therefore, landslide early warning systems (LEWS) are a helpful tool to depict the time and location of possible landslide events so that the hazardous situation can be managed more effectively. The main objective of this thesis is to set up a regional-scale LEWS that works in real-time over Catalonia (NE Spain). The developed warning system combines in real-time susceptibility information and rainfall observations to issue qualitative warnings over the region. Susceptibility has been derived combining slope angle and land use and land cover information with a simple fuzzy logic approach. The LEWS input rainfall information consists of high-resolution radar quantitative precipitation estimates (QPEs). To assess if a rainfall situation has the potential to trigger landslides, the LEWS applies a set of intensity duration thresholds. Finally, a warning matrix combines susceptibility and rainfall hazard to obtain a qualitative warning map that classifies the terrain into four warning classes. The evaluation of the LEWS performance has been challenging because of the lack of a systematic inventory, including the time and location of recent landslides events. Within the context of this thesis, a citizen-science initiative has been set up to gather landslide data from reports in social networks. However, some of the reports have significant spatial and temporal uncertainties. With the aim of finding the most suitable mapping unit for real-time warning purposes, the LEWS has been set-up to work using susceptibility maps based on grid-cells of different resolutions and subbasins. 30 m grid-cells have been chosen to compute the warnings as they offer a compromise between performance, interpretability of the results and computational costs. However, from an end users’ perspective visualising 30 m resolution warnings at a regional scale might be difficult. Therefore, subbasins have been proposed as a good option to summarise the warning outputs. A fuzzy verification method has been applied to evaluate the LEWS performance. Generally, the LEWS has been able to issue warnings in the areas where landslides were reported. The results of the fuzzy verification suggest that the LEWS effective resolution is around 1 km. The initial version of the LEWS has been improved by including soil moisture information in the characterisation of the rainfall situation. The outputs of this new approach have been compared with the outputs of LEWS using intensity-duration thresholds. With the new rainfall-soil moisture hydrometeorological thresholds, fewer false alarms were issued in high susceptibility areas where landslides had been observed. Therefore, hydrometeorological thresholds may be useful to improve the LEWS performance. This study provided a significant contribution to regional-scale landslide emergency management and risk mitigation in Catalonia. In addition, the modularity of the proposed LEWS makes it easy to apply in other regions.
Els lliscaments superficials i els corrents d’arrossegalls són un fenomen perillós que causa significants perdudes econòmiques i humanes arreu del món. La seva principal causa desencadenant és la pluja. La mitigació del risc degut a aquets processos a escala regional no es senzilla. Ena quest context, els sistemes d’alerta són una eina útil per tal de predir el lloc i el moment en que es poden desencadenar possibles esllavissades en el futur, i poder fer una gestió del risc més eficient. L’objectiu principal d’aquesta tesi és el desenvolupament d’un sistema d’alerta per esllavissades a escala regional, que treballi en temps real a Catalunya. El Sistema d’alerta que s’ha desenvolupat combina informació sobre la susceptibilitat del terreny i estimacions de la pluja d’alta resolució per donar unes alertes qualitatives arreu del territori. La susceptibilitat s’ha obtingut a partir de la combinació d’informació del pendent del terreny, i els usos i les cobertes del sòl utilitzant un mètode de lògica difusa. Les dades de pluja són observacions del radar meteorològic. Per tal d’analitzar si un determinat episodi de pluja te el potencial per desencadenar esllavissades, el sistema d’alerta utilitza un joc de llindars intensitat-durada. Posteriorment, una matriu d’alertes combina la susceptibilitat i la magnitud del episodi de pluja. El resultat, és un mapa d’alertes que classifica el terreny en quatre nivells d’alerta. Amb l’objectiu de definir quina unitat del terreny és la més adient pel càlcul de les alertes en temps real, el sistema d’alerta s’ha configurat per treballar utilitzant mapes de susceptibilitat basats en píxels de diverses resolucions, i en subconques. Finalment, l’opció més convenient és utilitzar píxels de 30 m, ja que ofereixen un compromís entre el funcionament, la facilitat d’interpretació dels resultats i el cost computacional. Tot i això, la visualització de les alertes a escala regional emprant píxels de 30 m pot ser difícil. Per això s’ha proposat utilitzar subconques per oferir un sumari de les alertes. Degut a la manca d’un inventari d’esllavissades sistemàtic, que contingui informació sobre el lloc i el moment en que les esllavissades es van desencadenar, l’avaluació del funcionament del sistema d’alerta ha sigut un repte. En el context d’aquesta tesi, s’ha creat una iniciativa per tal de recol·lectar dades d’esllavissades a partir de posts en xarxes socials. Malauradament, algunes d’aquestes dades estan afectades per incerteses espacials i temporals força importants. Per a l’avaluació el funcionament del sistema d’alerta, s’ha aplicat un mètode de verificació difusa. Generalment, els sistema d’alerta ha estat capaç de generar alertes a les zones on s’havien reportat esllavissades. Els resultats de la verificació difusa suggereixen que la resolució efectiva del sistema d’alerta età al voltant d’1 km. Finalment, la versió inicial del sistema d’alerta s’ha millorat per tal poder incloure informació sobre l’estat d’humitat del terreny en la caracterització de la magnitud del episodi de pluja. Els resultats del sistema d’alerta utilitzant aquest nou enfoc s’han comparat amb els resultats que s’obtenen al córrer el sistema d’alerta utilitzant els llindars intensitat-durada. Mitjançant els nous llindars hidrometeorològics, el sistema emet menys falses alarmes als llocs on s’han desencadenat esllavissades. Per tant, utilitzar llindars hidrometeorològics podria ser útil per millorar el funcionament del sistema d’alerta dissenyat. L’estudi dut a terme en aquesta tesi suposa una important contribució que pot ajudar en la gestió de les emergències degudes a esllavissades a escala regional a Catalunya. A més a més, el fet de que el sistema sigui modular permet la seva fàcil aplicació en d’altres regions en un futur.
Enginyeria del terreny
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Books on the topic "LANDSLIP SUSCEPTIBILITY MAPPING"

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1929-, Brabb Earl E., and Geological Survey (U.S.), eds. Map showing the status of landslide inventory and susceptibility mapping in California. [Reston, Va.?]: U.S. Dept. of the Interior, Geological Survey, 1986.

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Book chapters on the topic "LANDSLIP SUSCEPTIBILITY MAPPING"

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Bathrellos, George D., Dionissios P. Kalivas, and Hariklia D. Skilodimou. "Landslide Susceptibility Assessment Mapping." In Remote Sensing of Hydrometeorological Hazards, 493–512. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2017. http://dx.doi.org/10.1201/9781315154947-24.

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Nguyen, Chi Cong, Phuoc Vo, Viet Long Doan, Quang Binh Nguyen, Tien Cuong Nguyen, and Quoc Dinh Nguyen. "Assessment of the Effects of Rainfall Frequency on Landslide Susceptibility Mapping Using AHP Method: A Case Study for a Mountainous Region in Central Vietnam." In Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022, 87–98. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18471-0_7.

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AbstractVietnam’s mountainous regions often encounter landslides, frequently resulting in fatalities, infrastructure damage, and landscape destruction. A landslide susceptibility map is an effective tool for mitigating disaster impacts on hazard-prone areas. This study investigates the applicability of the Analytic Hierarchy Process to produce a landslide susceptibility index. Eight major impact factors are analyzed using SAGA, a GIS-based toolkit, including slopes, aspect, land use, soil type, elevation, distance to road, distance to stream, and antecedent rainfall. Four landslide susceptibility maps are produced corresponding to frequency scenarios of 3-day antecedent rainfall data which is taken from Regional Frequency Analysis (RFA). We assess the modeling performances using Area Under the Curve (AUC) index and the results show that the AHP model has good performance. The findings demonstrate a significant influence of rainfall antecedent conditions on the susceptibility map of landslides in this study area.
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Sarkar, Shantanu, and Debi Prasanna Kanungo. "GIS Application in Landslide Susceptibility Mapping of Indian Himalayas." In GIS Landslide, 211–19. Tokyo: Springer Japan, 2017. http://dx.doi.org/10.1007/978-4-431-54391-6_12.

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Panizza, M. "Landslide Susceptibility Mapping: A Methodological Approach." In Natural Disasters and Sustainable Development, 183–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-08905-7_11.

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Armas, Iuliana, Florica Stroia, and Laura Giurgea. "Statistic Versus Deterministic Method for Landslide Susceptibility Mapping." In Landslide Science and Practice, 383–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31310-3_52.

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Sengupta, Arnab, and Sankar Kumar Nath. "Landslide Susceptibility Mapping in Gangtok, Sikkim Himalaya." In Advances in Geographic Information Science, 539–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75197-5_24.

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Paulín, Gabriel Legorreta, Marcus Bursik, M. T. Ramírez-Herrera, J. Lugo-Hubp, J. J. Zamorano Orozco, and I. Alcántara-Ayala. "Landslide Inventory and Susceptibility Mapping in a Mexican Stratovolcano." In Landslide Science and Practice, 141–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31325-7_18.

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Scudero, Salvatore, and Giorgio De Guidi. "Landslide Processes and Susceptibility Mapping in NE Sicily, Italy." In Landslide Science and Practice, 493–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31325-7_64.

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Trigila, Alessandro, Paolo Frattini, Nicola Casagli, Filippo Catani, Giovanni Crosta, Carlo Esposito, Carla Iadanza, et al. "Landslide Susceptibility Mapping at National Scale: The Italian Case Study." In Landslide Science and Practice, 287–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31325-7_38.

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Høst, Jan, Marc-Henri Derron, and Kari Sletten. "Digital Rock-Fall and Snow Avalanche Susceptibility Mapping of Norway." In Landslide Science and Practice, 313–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31325-7_41.

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Conference papers on the topic "LANDSLIP SUSCEPTIBILITY MAPPING"

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Gaidzik, Krzysztof, Netra R. Regmi, Maria Teresa Ramirez-Herrera, and Ben Leshchinsky. "LANDSLIDE SUSCEPTIBILITY MAPPING IN GUERRERO, MEXICO." In GSA Annual Meeting in Denver, Colorado, USA - 2016. Geological Society of America, 2016. http://dx.doi.org/10.1130/abs/2016am-282794.

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Bernardo, E., V. Barrile, A. Fotia, and G. Bilotta. "Landslide susceptibility mapping with fuzzy methodology." In International Conference of Young Professionals «GeoTerrace-2020». European Association of Geoscientists & Engineers, 2020. http://dx.doi.org/10.3997/2214-4609.20205712.

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Zhao, Wenyi, Yuan Tian, Lun Wu, and Yu Liu. "Human impact index in landslide susceptibility mapping." In 2010 18th International Conference on Geoinformatics. IEEE, 2010. http://dx.doi.org/10.1109/geoinformatics.2010.5567817.

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Khabiri, Sahand, and Yichuan Zhu. "Uncertainty Quantification of Landslide Susceptibility Mapping Considering Landslide Boundary Geometry." In International Symposium for Geotechnical Safety & Risk. Singapore: Research Publishing Services, 2022. http://dx.doi.org/10.3850/978-981-18-5182-7_00-11-001.xml.

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Crawford, Matthew, Hudson Koch, Jason Dortch, Ashton A. Killen, and William C. Haneberg. "LANDSLIDE-SUSCEPTIBILITY MAPPING AND RISK ASSESSMENT, EASTERN KENTUCKY." In GSA 2020 Connects Online. Geological Society of America, 2020. http://dx.doi.org/10.1130/abs/2020am-355833.

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Ray, Ram L., and Jennifer M. Jacobs. "Landslide Susceptibility Mapping using Remotely Sensed Soil Moisture." In IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2008. http://dx.doi.org/10.1109/igarss.2008.4779279.

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Badola, Shubham, Varun Narayan Mishra, and Surya Parkash. "Landslide susceptibility mapping using XGBoost machine learning method." In 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS). IEEE, 2023. http://dx.doi.org/10.1109/migars57353.2023.10064496.

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Niraj, K. C., Ankit Singh, and Dericks Praise Shukla. "Improved Landslide Susceptibility mapping using statistical MLR model." In 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS). IEEE, 2023. http://dx.doi.org/10.1109/migars57353.2023.10064594.

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Chen, Tao, Ziying Zhong, and Ruiqing Niu. "Landslide spatial susceptibility mapping by using deep belief network." In 2018 Fifth International Workshop on Earth Observation and Remote Sensing Applications (EORSA). IEEE, 2018. http://dx.doi.org/10.1109/eorsa.2018.8598636.

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Khatun, Mahmuda, A. T. M. Shakhawat Hossain, and Mir Fazlul Karim. "LANDSLIDE SUSCEPTIBILITY MAPPING OF SOUTH-EASTERN RANGAMATI DISTRICT, BANGLADESH." In GSA Annual Meeting in Indianapolis, Indiana, USA - 2018. Geological Society of America, 2018. http://dx.doi.org/10.1130/abs/2018am-316135.

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Reports on the topic "LANDSLIP SUSCEPTIBILITY MAPPING"

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Metz, L., and A. N. Bear-Crozier. Landslide susceptibility mapping: a remote sensing based approach using QGIS 2.2 (Valmiera): technical manual. Geoscience Australia, 2014. http://dx.doi.org/10.11636/record.2014.056.

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Blais-Stevens, A., M. Kremer, A. Page, and R. Couture. Regional landslide susceptibility mapping along the Yukon Alaska highway corridor: A qualitative heuristic approach. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2011. http://dx.doi.org/10.4095/288986.

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Growney, Lawrence. Landslide Inventory and Susceptibility Mapping of the Upper Canyon Creek Basin, Cascade Range, Skamania County, Washington. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6892.

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Mickelson, Katherine. LiDAR-Based Landslide Inventory and Susceptibility Mapping, and Differential LiDAR Analysis for the Panther Creek Watershed, Coast Range, Oregon. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.253.

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