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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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樓主: advocate
21#
發(fā)表于 2025-3-25 04:03:27 | 只看該作者
Springer Nature Switzerland AG 2019
22#
發(fā)表于 2025-3-25 10:22:55 | 只看該作者
Geoinformatics and Modelling of Landslide Susceptibility and Risk978-3-030-10495-5Series ISSN 1863-5520 Series E-ISSN 1863-5539
23#
發(fā)表于 2025-3-25 14:40:39 | 只看該作者
https://doi.org/10.1007/978-3-030-10495-5Landslide Susceptibility and Risk; Geoinformatics and Landslide; Semi-quantitative Approaches and land
24#
發(fā)表于 2025-3-25 17:39:21 | 只看該作者
25#
發(fā)表于 2025-3-25 20:13:40 | 只看該作者
26#
發(fā)表于 2025-3-26 00:46:13 | 只看該作者
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
27#
發(fā)表于 2025-3-26 07:10:56 | 只看該作者
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
28#
發(fā)表于 2025-3-26 12:17:20 | 只看該作者
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,
29#
發(fā)表于 2025-3-26 16:06:23 | 只看該作者
30#
發(fā)表于 2025-3-26 20:00:31 | 只看該作者
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
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