Landslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage (Ds d) , distance to faults (Ds f) , drainage density (Dd) , elevation (Elev), fault density (Fd) , lithology, normalized difference vegetation index (NDVI), plan curvature (Pl c) , profile curvature (Pr c) , slope (S∘), stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions (< 32% of total area) were at a higher risk to landslide compared to the center, west, and northwest of the area (> 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results. Graphical Abstract: [Figure not available: see fulltext.].

Evaluation of the landslide susceptibility and its spatial difference in the whole Qinghai-Tibetan Plateau region by five learning algorithms

Bonafoni S.;
2022

Abstract

Landslides are considered as major natural hazards that cause enormous property damages and fatalities in Qinghai-Tibetan Plateau (QTP). In this article, we evaluated the landslide susceptibility, and its spatial differencing in the whole Qinghai-Tibetan Plateau region using five state-of-the-art learning algorithms; deep neural network (DNN), logistic regression (LR), Naïve Bayes (NB), random forest (RF), and support vector machine (SVM), differing from previous studies only in local areas of QTP. The 671 landslide events were considered, and thirteen landslide conditioning factors (LCFs) were derived for database generation, including annual rainfall, distance to drainage (Ds d) , distance to faults (Ds f) , drainage density (Dd) , elevation (Elev), fault density (Fd) , lithology, normalized difference vegetation index (NDVI), plan curvature (Pl c) , profile curvature (Pr c) , slope (S∘), stream power index (SPI), and topographic wetness index (TWI). The multi-collinearity analysis and mean decrease Gini (MDG) were used to assess the suitability and predictability of these factors. Consequently, five landslide susceptibility prediction (LSP) maps were generated and validated using accuracy, area under the receiver operatic characteristic curve, sensitivity, and specificity. The MDG results demonstrated that the rainfall, elevation, and lithology were the most significant landslide conditioning factors ruling the occurrence of landslides in Qinghai-Tibetan Plateau. The LSP maps depicted that the north-northwestern and south-southeastern regions (< 32% of total area) were at a higher risk to landslide compared to the center, west, and northwest of the area (> 45% of total area). Moreover, among the five models with a high goodness-of-fit, RF model was highlighted as the superior one, by which higher accuracy of landslide susceptibility assessment and better prone areas management in QTP can be achieved compared to previous results. Graphical Abstract: [Figure not available: see fulltext.].
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/1507251
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