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Titlebook: Statistical Approaches for Landslide Susceptibility Assessment and Prediction; Sujit Mandal,Subrata Mondal Book 2019 Springer Internationa

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楼主: Truman
发表于 2025-3-27 00:45:53 | 显示全部楼层
Geomorphic, Geo-tectonic, and Hydrologic Attributes and Landslide Probability,ove down slope. Darjeeling Himalaya exhibits a wide range of all these parameters and as a result of which slope failure has become a quite common phenomena in every monsoon which inflicts a great damage to human lives and properties. To mitigate the landslides and its destructive nature, the relati
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Artificial Neural Network (ANN) Model and Landslide Susceptibility,l network (ANN) model. Fifteen landslide causative factors, i.e. elevation, slope aspect, slope angle, slope curvature, geology, soil, lineament density, distance to lineament, drainage density, distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalized diff
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Knowledge-Driven Statistical Approach for Landslide Susceptibility Assessment Using GIS and Fuzzy Lenvironment. To accomplish fuzzy logic, several data layers such as elevation, slope, aspect, curvature, drainage density, distance to drainage, lineament density, distance to lineament, lithology, land use and land cover, soil, stream power index (SPI), topographic wetness index (TWI), and rainfall
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Comparison Between Statistical Models: A Review and Evaluation,esent study, various geomorphic and geohydrologic parameters, i.e. elevation, slope aspect, slope angle, slope curvature, geology, soil, lineament density, distance to lineament, drainage density, distance to drainage, stream power index (SPI), topographic wetted index (TWI), rainfall, normalized di
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Book 2019uch 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 susceptibili
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