Application of GIS-Based data-driven bivariate statistical models for Landslide prediction: A case study of highly affected landslide prone areas of Teesta River basin

[1]  R. Roy,et al.  An integrated GIS, MIF, and TOPSIS approach for appraising electric vehicle charging station suitability zones in Mumbai, India , 2023, Sustainable Cities and Society.

[2]  P. Kundu,et al.  An Ensemble of Evidence Belief Function (Ebf) with Frequency Ratio (Fr) Using Geospatial Data for Landslide Prediction in Darjeeling Himalayan Region of India , 2023, SSRN Electronic Journal.

[3]  M. D. Behera,et al.  Geomorphic response of bedrock landslides induced landscape evolution across the Teesta catchment, Eastern Himalaya , 2023, Environmental Earth Sciences.

[4]  T. Fernández,et al.  Landslide susceptibility mapping using maximum entropy (MaxEnt) and geographically weighted logistic regression (GWLR) models in the Río Aguas catchment (Almería, SE Spain) , 2023, Natural Hazards.

[5]  Md. Sharafat Chowdhury.  Modelling hydrological factors from DEM using GIS , 2023, MethodsX.

[6]  Rajib Mitra,et al.  A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills region of Eastern India , 2022, Environmental Science and Pollution Research.

[7]  Hanwei Xu,et al.  The Identification and Analysis of the Centers of Geographical Public Opinions in Flood Disasters Based on Improved Naïve Bayes Network , 2022, International journal of environmental research and public health.

[8]  T. Nguyen,et al.  Post-failure process and kinematic behavior of two landslides: Case study and material point analyses , 2022, Computers and Geotechnics.

[9]  S. K. Singh,et al.  A GIS-based Spatial Prediction of Landslide Hazard Zones and Mapping in an Eastern Himalayan Hilly Region Using Large Scale Soil Mapping and Analytical Hierarchy Process , 2022, Journal of the Indian Society of Remote Sensing.

[10]  D. Shukla,et al.  Effect of scale and mapping unit on landslide susceptibility mapping of Mandakini River Basin, Uttarakhand, India , 2022, Environmental Earth Sciences.

[11]  Duong Tran Anh,et al.  An ensemble random forest tree with SVM, ANN, NBT, and LMT for landslide susceptibility mapping in the Rangit River watershed, India , 2022, Natural Hazards.

[12]  A. Zafar,et al.  Comparison of multiple conventional and unconventional machine learning models for landslide susceptibility mapping of Northern part of Pakistan , 2022, Environment, Development and Sustainability.

[13]  H. Hong,et al.  Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold , 2022, Geomorphology.

[14]  Ebrahim Ghaderpour,et al.  Wildfire Risk Forecasting Using Weights of Evidence and Statistical Index Models , 2022, Sustainability.

[15]  D. P. Kanungo,et al.  GIS-based landslide susceptibility zonation mapping using the analytic hierarchy process (AHP) method in parts of Kalimpong Region of Darjeeling Himalaya , 2022, Environmental Monitoring & Assessment.

[16]  A. Sengupta,et al.  Geomorphic evaluation of landslides along the Teesta river valley, Sikkim Himalaya, India , 2022, Geological Journal.

[17]  Hamid Reza Pourghasemi,et al.  A comparative assessment of gully erosion spatial predictive modeling using statistical and machine learning models , 2021 .

[18]  P. Atkinson,et al.  Deep learning-based landslide susceptibility mapping , 2021, Scientific Reports.

[19]  Amit K. Shrivastava,et al.  Landslide hazard assessment using analytic hierarchy process (AHP): A case study of National Highway 5 in India , 2021, Ain Shams Engineering Journal.

[20]  H. Tanyaş,et al.  Supplementary material to "Insights from the topographic characteristics of a large global catalog of rainfall-induced landslide event inventories" , 2021 .

[21]  Hao Wang,et al.  Bagging-based machine learning algorithms for landslide susceptibility modeling , 2021, Natural Hazards.

[22]  Xinzhi Zhou,et al.  Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization , 2021 .

[23]  Jianping Wu,et al.  A hybrid optimization method of factor screening predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping , 2021 .

[24]  Yun-Tae Kim,et al.  Landslide spatial probability prediction: a comparative assessment of naïve Bayes, ensemble learning, and deep learning approaches , 2021, Bulletin of Engineering Geology and the Environment.

[25]  Hamid Reza Pourghasemi,et al.  Landslide susceptibility mapping using statistical bivariate models and their hybrid with normalized spatial-correlated scale index and weighted calibrated landslide potential model , 2021, Environmental Earth Sciences.

[26]  Chong Xu,et al.  Calculation of landslide occurrence probability in Taiwan region under different ground motion conditions , 2021, Journal of Mountain Science.

[27]  Thomas Blaschke,et al.  Decision tree based ensemble machine learning approaches for landslide susceptibility mapping , 2021, Geocarto International.

[28]  V. Sridhar,et al.  Landslide Susceptibility Analysis: A Logistic Regression Model Case Study in Coonoor, India , 2021 .

[29]  Guilherme Garcia de Oliveira,et al.  Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using Artificial Neural Networks , 2021 .

[30]  Teiji Watanabe,et al.  Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions , 2021, ISPRS Int. J. Geo Inf..

[31]  Prafull Singh,et al.  Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand, India , 2021, Environment, Development and Sustainability.

[32]  Yasin Wahid Rabby,et al.  Landslide susceptibility mapping in three Upazilas of Rangamati hill district Bangladesh: application and comparison of GIS-based machine learning methods , 2020, Geocarto International.

[33]  Binh Thai Pham,et al.  Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping , 2020 .

[34]  Biswajeet Pradhan,et al.  An ensemble model for landslide susceptibility mapping in a forested area , 2020, Geocarto International.

[35]  A. L. Achu,et al.  Landslide susceptibility modelling using integrated evidential belief function based logistic regression method: A study from Southern Western Ghats, India , 2020 .

[36]  M. G. Lenzano,et al.  Analytic hierarchy process applied to landslide susceptibility mapping of the North Branch of Argentino Lake, Argentina , 2020, Natural Hazards.

[37]  Mohammad Ali Ghorbani,et al.  GIS-based landslide susceptibility modeling: A comparison between fuzzy multi-criteria and machine learning algorithms , 2020, Geoscience Frontiers.

[38]  Ali P. Yunus,et al.  Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance , 2020 .

[39]  Deliang Sun,et al.  A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm , 2020, Geomorphology.

[40]  Nadhir Al-Ansari,et al.  Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment , 2020, International journal of environmental research and public health.

[41]  Prafull Singh,et al.  Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India , 2020, Environment, Development and Sustainability.

[42]  S. Mandal,et al.  Data-driven evidential belief function (EBF) model in exploring landslide susceptibility zones for the Darjeeling Himalaya, India , 2020, Geocarto International.

[43]  Pijush Samui,et al.  Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area , 2020 .

[44]  P. Confuorto,et al.  Machine learning ensemble modelling as a tool to improve landslide susceptibility mapping reliability , 2020, Landslides.

[45]  M. Meten,et al.  Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia , 2020, SN Applied Sciences.

[46]  Yanli Wu,et al.  Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping , 2020 .

[47]  Nadhir Al-Ansari,et al.  Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms , 2020, International journal of environmental research and public health.

[48]  O. Igwe,et al.  Erodibility characteristics and slope stability analysis of geological units prone to erosion in Udi area, southeast Nigeria , 2020, Modeling Earth Systems and Environment.

[49]  Wei Chen,et al.  Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping , 2020, Symmetry.

[50]  S. Panchal,et al.  Application of analytic hierarchy process in landslide susceptibility mapping at regional scale in GIS environment , 2020, Journal of Statistics and Management Systems.

[51]  Wei Chen,et al.  Landslide Susceptibility Evaluation Using Hybrid Integration of Evidential Belief Function and Machine Learning Techniques , 2019, Water.

[52]  B. K. Bhadra,et al.  Landslide Susceptibility Mapping Using Landslide Numerical Risk Factor Model and Landslide Inventory Prepared Through OBIA in Chenab Valley, Jammu and Kashmir (India) , 2019, Journal of the Indian Society of Remote Sensing.

[53]  Thomas Blaschke,et al.  A Novel Ensemble Approach for Landslide Susceptibility Mapping (LSM) in Darjeeling and Kalimpong Districts, West Bengal, India , 2019, Remote. Sens..

[54]  Nayyer Saleem,et al.  Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review , 2019, ISPRS Int. J. Geo Inf..

[55]  Ebru Akcapinar Sezer,et al.  Landslide susceptibility mapping of the Mediterranean coastal zone of Morocco between Oued Laou and El Jebha using artificial neural networks (ANN) , 2019, Arabian Journal of Geosciences.

[56]  Ali P. Yunus,et al.  Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan , 2019, Landslides.

[57]  Neelima Satyam,et al.  Estimation of Rainfall-Induced Landslides Using the TRIGRS Model , 2019, Earth Systems and Environment.

[58]  Sadhan Malik,et al.  Potential Landslide Vulnerability Zonation Using Integrated Analytic Hierarchy Process and GIS Technique of Upper Rangit Catchment Area, West Sikkim, India , 2019, Journal of the Indian Society of Remote Sensing.

[59]  Wei Chen,et al.  Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree , 2019, Geocarto International.

[60]  Shouyun Liang,et al.  Landslide susceptibility assessment using different slope units based on the evidential belief function model , 2019, Geocarto International.

[61]  Hyung-Sup Jung,et al.  Landslide susceptibility mapping using Naïve Bayes and Bayesian network models in Umyeonsan, Korea , 2019, Geocarto International.

[62]  S. Pal,et al.  GIS-based spatial prediction of landslide susceptibility using frequency ratio model of Lachung River basin, North Sikkim, India , 2019, SN Applied Sciences.

[63]  Shouyun Liang,et al.  Landslide susceptibility assessment using evidential belief function, certainty factor and frequency ratio model at Baxie River basin, NW China , 2019 .

[64]  John Muschelli,et al.  ROC and AUC with a Binary Predictor: a Potentially Misleading Metric , 2019, Journal of Classification.

[65]  Biswajeet Pradhan,et al.  GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms , 2019, Journal of Mountain Science.

[66]  Alex J. Bowers,et al.  Receiver Operating Characteristic (ROC) Area Under the Curve (AUC): A Diagnostic Measure for Evaluating the Accuracy of Predictors of Education Outcomes , 2019, Journal of Education for Students Placed at Risk (JESPAR).

[67]  Qiqing Wang,et al.  Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor , 2019, Geomatics, Natural Hazards and Risk.

[68]  S. Mandal,et al.  Landslide susceptibility mapping of Darjeeling Himalaya, India using index of entropy (IOE) model , 2018, Applied Geomatics.

[69]  Jie Liu,et al.  Quantitative Assessment of Landslide Susceptibility Comparing Statistical Index, Index of Entropy, and Weights of Evidence in the Shangnan Area, China , 2018, Entropy.

[70]  Vijendra Kumar Pandey,et al.  Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya , 2018, Geocarto International.

[71]  Himan Shahabi,et al.  A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment , 2018, Geocarto International.

[72]  H. Yamagishi,et al.  Landslide Susceptibility Mapping in Tegucigalpa, Honduras, Using Data Mining Methods , 2018, IAEG/AEG Annual Meeting Proceedings, San Francisco, California, 2018 - Volume 1.

[73]  Christos Polykretis,et al.  Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models , 2018, Natural Hazards.

[74]  K. Yin,et al.  Rainfall Threshold Analysis and Bayesian Probability Method for Landslide Initiation Based on Landslides and Rainfall Events in the Past , 2018 .

[75]  Mousa Abedini,et al.  Assessing LNRF, FR, and AHP models in landslide susceptibility mapping index: a comparative study of Nojian watershed in Lorestan province, Iran , 2018, Environmental Earth Sciences.

[76]  P. Reichenbach,et al.  A review of statistically-based landslide susceptibility models , 2018 .

[77]  T. Cheng,et al.  Mapping landslide susceptibility and types using Random Forest , 2018 .

[78]  Ebru Eris,et al.  Threshold contour production of rainfall intensity that induces landslides in susceptible regions of northern Turkey , 2018, Landslides.

[79]  Biswajeet Pradhan,et al.  Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods , 2018 .

[80]  S. Mandal,et al.  RS & GIS-based landslide susceptibility mapping of the Balason River basin, Darjeeling Himalaya, using logistic regression (LR) model , 2018 .

[81]  Prima Riza Kadavi,et al.  Evaluation of landslide susceptibility mapping by evidential belief function, logistic regression and support vector machine models , 2018 .

[82]  Qian Wang,et al.  Integration of Information Theory, K-Means Cluster Analysis and the Logistic Regression Model for Landslide Susceptibility Mapping in the Three Gorges Area, China , 2017, Remote. Sens..

[83]  Wei Li,et al.  Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models , 2017 .

[84]  Ying Wang,et al.  Landslide susceptibility mapping on a global scale using the method of logistic regression , 2017 .

[85]  Yacine Achour,et al.  Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria , 2017, Arabian Journal of Geosciences.

[86]  Yanli Wu,et al.  A comparative study on the landslide susceptibility mapping using logistic regression and statistical index models , 2017, Arabian Journal of Geosciences.

[87]  Hengxing Lan,et al.  A modified frequency ratio method for landslide susceptibility assessment , 2017, Landslides.

[88]  Johnny Alexander Vega,et al.  Quantitative risk assessment of landslides triggered by earthquakes and rainfall based on direct costs of urban buildings , 2016 .

[89]  Qiuyan Yu,et al.  Feasibility Study of Land Cover Classification Based on Normalized Difference Vegetation Index for Landslide Risk Assessment , 2016 .

[90]  Guifang Zhang,et al.  Integration of the Statistical Index Method and the Analytic Hierarchy Process technique for the assessment of landslide susceptibility in Huizhou, China , 2016 .

[91]  Fan Yang,et al.  GIS-based landslide susceptibility analysis using frequency ratio and evidential belief function models , 2016, Environmental Earth Sciences.

[92]  S. Bhattacharya.  A comparison of the predicted vulnerability zones with the data based on hazard zones of landslide in the Kurseong hill subdivision, Darjeeling district, West Bengal, India , 2016, Environmental Earth Sciences.

[93]  Qiqing Wang,et al.  A comparative study on the landslide susceptibility mapping using evidential belief function and weights of evidence models , 2016, Journal of Earth System Science.

[94]  Wei Chen,et al.  Application of frequency ratio, weights of evidence and evidential belief function models in landslide susceptibility mapping , 2016 .

[95]  Wei Chen,et al.  A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping , 2016, Arabian Journal of Geosciences.

[96]  Dimitrios Myronidis,et al.  Landslide susceptibility mapping based on landslide history and analytic hierarchy process (AHP) , 2016, Natural Hazards.

[97]  H. Pourghasemi,et al.  Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran , 2016, Environmental Earth Sciences.

[98]  B. Pradhan,et al.  Rainfall-induced landslide susceptibility assessment at the Chongren area (China) using frequency ratio, certainty factor, and index of entropy , 2016 .

[99]  P. Burlando,et al.  An advanced process‐based distributed model for the investigation of rainfall‐induced landslides: The effect of process representation and boundary conditions , 2015 .

[100]  Ning Lu,et al.  Hydrological factors affecting rainfall-induced shallow landslides: From the field monitoring to a simplified slope stability analysis , 2015 .

[101]  Ryuichi Yatabe,et al.  Effect of Landslide Factor Combinations on the Prediction Accuracy of Landslide Susceptibility Maps in the Blue Nile Gorge of Central Ethiopia , 2015, Geoenvironmental Disasters.

[102]  B. Pradhan,et al.  Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models , 2015, Geosciences Journal.

[103]  A. Torkashvand,et al.  The preparation of landslide map by Landslide Numerical Risk Factor (LNRF) model and Geographic Information System (GIS) , 2014 .

[104]  A. Zhu,et al.  An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic , 2014 .

[105]  T. Kavzoglu,et al.  Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression , 2014, Landslides.

[106]  Yannick Thiery,et al.  Application of expert rules in indirect approaches for landslide susceptibility assessment , 2014, Landslides.

[107]  H. Shahabi,et al.  Landslide susceptibility mapping at central Zab basin, Iran: a comparison between analytical hierarchy process, frequency ratio and logistic regression models , 2014 .

[108]  M. Ghose,et al.  Application of frequency ratio and likelihood ratio model for geo-spatial modelling of landslide hazard vulnerability assessment and zonation: a case study from the Sikkim Himalayas in India , 2014 .

[109]  Jung Hyun Lee,et al.  A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping , 2014 .

[110]  E. Sujatha,et al.  Assessing landslide susceptibility using Bayesian probability-based weight of evidence model , 2014, Bulletin of Engineering Geology and the Environment.

[111]  Shraban Sarkar,et al.  Landslide susceptibility assessment using Information Value Method in parts of the Darjeeling Himalayas , 2013, Journal of the Geological Society of India.

[112]  B. Pradhan,et al.  Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran , 2013, Arabian Journal of Geosciences.

[113]  Y. Hong,et al.  Susceptibility evaluation and mapping of China’s landslides based on multi-source data , 2013, Natural Hazards.

[114]  H. Pourghasemi,et al.  Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances , 2013, Natural Hazards.

[115]  A. Ozdemir,et al.  A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey , 2013 .

[116]  E. Sujatha,et al.  Landslide susceptibility analysis using probabilistic likelihood ratio model—a geospatial-based study , 2013, Arabian Journal of Geosciences.

[117]  B. Neuhäuser,et al.  GIS-based assessment of landslide susceptibility on the base of the Weights-of-Evidence model , 2012, Landslides.

[118]  B. Pradhan,et al.  Landslide susceptibility mapping at Golestan Province, Iran: A comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models , 2012 .

[119]  G. Victor Rajamanickam,et al.  Landslide susceptibility analysis using Probabilistic Certainty Factor Approach: A case study on Tevankarai stream watershed, India , 2012, Journal of Earth System Science.

[120]  Khayingshing Luirei,et al.  Landslides and neotectonic activities in the Main Boundary Thrust (MBT) zone: Southeastern Kumaun, Uttarakhand , 2012, Journal of the Geological Society of India.

[121]  B. Pradhan,et al.  Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran , 2012, Natural Hazards.

[122]  M. Martina,et al.  Probabilistic rainfall thresholds for landslide occurrence using a Bayesian approach , 2012 .

[123]  A. Akgun.  A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey , 2012, Landslides.

[124]  Iuliana Armaş,et al.  Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania , 2012, Natural Hazards.

[125]  C. Westen,et al.  Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India) , 2011 .

[126]  D. P. Kanungo,et al.  Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides , 2011 .

[127]  Biswajeet Pradhan,et al.  Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia , 2011 .

[128]  M. Bednarik,et al.  Landslide susceptibility assessment using the bivariate statistical analysis and the index of entropy in the Sibiciu Basin (Romania) , 2011 .

[129]  B. Pradhan,et al.  Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area , 2010 .

[130]  A. Shakoor,et al.  A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses , 2010 .

[131]  Isik Yilmaz,et al.  Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat - Turkey) , 2009, Comput. Geosci..

[132]  B. Pradhan,et al.  Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models , 2007 .

[133]  Chang-Jo Chung,et al.  Two models for evaluating landslide hazards , 2006, Comput. Geosci..

[134]  E. Yesilnacar,et al.  Landslide susceptibility mapping : A comparison of logistic regression and neural networks methods in a medium scale study, Hendek Region (Turkey) , 2005 .

[135]  L. Ermini,et al.  Artificial Neural Networks applied to landslide susceptibility assessment , 2005 .

[136]  Saro Lee.  Application of Likelihood Ratio and Logistic Regression Models to Landslide Susceptibility Mapping Using GIS , 2004, Environmental management.

[137]  Jan Otto Larsen,et al.  Landslide hazards and mitigation measures at Gangtok, Sikkim Himalaya , 2002 .

[138]  K. Beven,et al.  A physically based, variable contributing area model of basin hydrology , 1979 .

[139]  Arthur P. Dempster,et al.  Upper and Lower Probabilities Induced by a Multivalued Mapping , 1967, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[140]  C. Chi,et al.  The Effect of the Normalized Difference Vegetation Index to Landslide Susceptibility using Optical Imagery Sentinel 2 and Landsat 8 , 2021, 4th Asia Pacific Meeting on Near Surface Geoscience & Engineering.

[141]  A. Sapkota,et al.  Food insecurity and compound environmental shocks in Nepal: Implications for a changing climate. , 2021, World development.

[142]  Bahareh Kalantar,et al.  Landslide Susceptibility Modeling: An Integrated Novel Method Based on Machine Learning Feature Transformation , 2021, Remote. Sens..

[143]  J.M.V. Grzybowski,et al.  Landslide susceptibility mapping with r.landslide: A free open-source GIS-integrated tool based on Artificial Neural Networks , 2020, Environ. Model. Softw..

[144]  S. Mandal,et al.  Prediction of Landslide Susceptibility Using Bivariate Models , 2019, Geoinformatics and Modelling of Landslide Susceptibility and Risk.

[145]  F. Vahedifard,et al.  Rainfall-triggered slope instabilities under a changing climate: comparative study using historical and projected precipitation extremes , 2017 .

[146]  Murat Ercanoglu,et al.  Landslide identification and classification by object-based image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey) , 2012, Comput. Geosci..

[147]  Steve Parry,et al.  The Application of Geomorphological Mapping in the Assessment of Landslide Hazard in Hong Kong , 2011 .

[148]  No-Wook Park,et al.  Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis , 2011 .

[149]  J. Malik,et al.  Active Faulting and Deformation of Quaternary Landform Sub-Himalaya, India , 2010 .

[150]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .