Titlebook: Geoinformatics and Modelling of Landslide Susceptibility and Risk; An RS & GIS-based Mo Sujit Mandal,Subrata Mondal Book 2019 Springer Natu

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Springer Nature Switzerland AG 2019

Tartar

Geoinformatics and Modelling of Landslide Susceptibility and Risk978-3-030-10495-5Series ISSN 1863-5520 Series E-ISSN 1863-5539

汇总

https://doi.org/10.1007/978-3-030-10495-5Landslide Susceptibility and Risk; Geoinformatics and Landslide; Semi-quantitative Approaches and land

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仔细阅读

antiquated

Slope Instability Analysis Using Morphometric Parameters: A Sub-watersheds Scale Study,the structure, the planform and the relief of basin which are being applied for the prioritization of watersheds. In the present study, an attempt has been made to prioritize sub-watersheds based on morphometric analysis in relation to slope instability. The base map of stream network were digitized

PRO

Geomorphic Diversity and Landslide Susceptibility: A Multi-criteria Evaluation Approach,to find out the role of drainage parameters and relief parameters in slope failure using drainage diversity (DD) and relief diversity (RD) models respectively. For that total 14 morphometric data layers were considered. The relationship of each data layers with landslide susceptibility was judge usi

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Prediction of Landslide Susceptibility Using Bivariate Models,A) and statistical index model (SIM) and the preparation of landslide susceptibility maps of the Balason river basin of Darjeeling Himalaya using various geomorphic, hydrologic, and tectonic attributes such as elevation, slope, aspect, curvature, geology, geomorphology, soil, distance to lineament,

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Machine Learning Models and Spatial Distribution of Landslide Susceptibility, tools machine learning model i.e. support vector machine (SVM) and artificial neural network model (ANNM). Fifteen landslide causative factors i.e. slope, aspect, curvature, elevation, geology, geomorphology, soil, distance to drainage, drainage density, distance to lineaments, lineament density, l