About this book
This book focuses on the spatial distribution of landslide hazards of the Darjeeling Himalayas. Knowledge driven methods and statistical techniques such as frequency ratio model (FRM), information value model (IVM), logistic regression model (LRM), index overlay model (IOM), certainty factor model (CFM), analytical hierarchy process (AHP), artificial neural network model (ANN), and fuzzy logic have been adopted to identify landslide susceptibility. In addition, a comparison between various statistical models were made using success rate cure (SRC) and it was found that artificial neural network model (ANN), certainty factor model (CFM) and frequency ratio based fuzzy logic approach are the most reliable statistical techniques in the assessment and prediction of landslide susceptibility in the Darjeeling Himalayas. The study identified very high, high, moderate, low and very low landslide susceptibility locations to take site-specific management options as well as to ensure developmental activities in theDarjeeling Himalayas.
Particular attention is given to the assessment of various geomorphic, geotectonic and geohydrologic attributes that help to understand the role of different factors and corresponding classes in landslides, to apply different models, and to monitor and predict landslides. The use of various statistical and physical models to estimate landslide susceptibility is also discussed. The causes, mechanisms and types of landslides and their destructive character are elaborated in the book. Researchers interested in applying statistical tools for hazard zonation purposes will find the book appealing.
- Book Title Statistical Approaches for Landslide Susceptibility Assessment and Prediction
- DOI https://doi.org/10.1007/978-3-319-93897-4
- Copyright Information Springer International Publishing AG, part of Springer Nature 2019
- Publisher Name Springer, Cham
- eBook Packages Earth and Environmental Science Earth and Environmental Science (R0)
- Hardcover ISBN 978-3-319-93896-7
- Softcover ISBN 978-3-030-06739-7
- eBook ISBN 978-3-319-93897-4
- Edition Number 1
- Number of Pages XIII, 193
- Number of Illustrations 5 b/w illustrations, 61 illustrations in colour
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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