GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India
TL;DR: In this article , the authors compared the landslide susceptibility maps (LSMs) prepared from five GIS-based data-driven bivariate statistical models, namely, (a) Frequency Ratio (FR), (b) Index of Entropy (IOE), (c) Statistical Index (SI), (d) Modified Information Value Model (MIV) and (e) Evidential Belief Function (EBF).
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About: This article is published in Heliyon. The article was published on 01 May 2023. and is currently open access. The article focuses on the topics: Medicine & Landslide.
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Citations
Application of index of entropy and Geospatial techniques for landslide prediction in Lunglei district, Mizoram, India
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Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat-Turkey)
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