Statistical Approaches for Landslide Susceptibility Assessment and Prediction (eBook)

Professor Sujit Mandal is a Professor of Geography at Diamond Harbour Women’s University, West Bengal, India. He is a former Professor at University of Gour Banga, West Bengal, India, and has been serving in colleges and Universities since 2005. He completed his B.Sc. and M.Sc. from Calcutta University. His Ph.D. thesis was entitled “An analysis of Slope instability in the Shivkhola Watershed of Darjeeling Himalaya: A scientific approach towards the management of land, water and soil”. Professor Mandal is specialised in applied geomorphology, environmental hazards and geoinformatics. During his teaching career, Professor Mandal has participated and presented research papers in different National and International seminars/conferences on several aspects of geomorphic processes. He is the author of 40 research articles published from reputed national and international journals. He is the author of the book ‘Semi-quantitative approaches for Landslide Susceptibility Assessment and Prediction’ published by Springer. He is the principal investigator of one Minor Research Project sponsored by UGC and one Major Research Project sponsored by ICSSR. Mr. Subrata Mondal completed his B.A and M.A. from the University of Gour Banga, West Bengal, India. He is the author of several peer-reviewed research articles in reputable international journals. He is proficient in Geoinformatics and model development. Mr. Mondal completed his M.Phil. degree entitled “Slope instability analysis and delineation of potential landslide susceptibility zones of the Balason river basin in Darjeeling Himalaya, West Bengal” from the University of Gour Banga, West Bengal in 2017. He participated in several national and international seminars and conferences and presented research papers on Landslides in Darjeeling Himalaya.

1. Concept on Landslides and Landslide Susceptibility2. Geomorphic, Geo-tectonic and Hydrologic Attributes and   Landslide Probability3. Frequency Ratio Model (FRM) and Information Value Model (IVM) in landslide susceptibility Assessment and Prediction4. Logistic Regression Model (LRM) and Landslide Susceptibility: A RS & GIS based approach5. Artificial Neural Network (ANN) Model and Landslide Susceptibility6. Weighted Index Overlay Model (WIOM), Certainty factor approach (CFA) and Analytical Hierarchy Process (AHP) in Landslide Susceptibility Studies7. Knowledge driven Statistical Approach for Landslide Susceptibility Assessment using GIS and Fuzzy Logic8. Comparison between Statistical Models: A Review and EvaluationIndex