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

• Published in: Geoscience letters Vol. 9; no. 1; pp. 1 - 25
• Main Authors: Sajadi, Payam, Sang, Yan-Fang, Gholamnia, Mehdi, Bonafoni, Stefania, Mukherjee, Saumitra
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 14.02.2022; Springer Nature B.V; SpringerOpen
• Subjects: Accuracy; Algorithms; Annual rainfall; Artificial neural networks; Atmospheric Sciences; Bayesian analysis; Biogeosciences; Collinearity; Curvature; Distance; Drainage; Drainage density; Earth and Environmental Science; Earth Sciences; Elevation; Evaluation; Feature selection technique; Geophysics/Geodesy; Goodness of fit; Landslide susceptibility; Landslides; Landslides & mudslides; Learning algorithms; Lithology; Machine learning; Machine learning algorithm; Neural networks; Normalized difference vegetative index; Oceanography; Planetology; Plateaus; Property damage; Qinghai-Tibetan Plateau; Rain; Research Letter; Spatial differencing; Specificity; Support vector machines; Susceptibility; Tibetan Plateau; Landslide susceptibility; Feature selection technique; Spatial differencing; Machine learning algorithm; Qinghai-Tibetan Plateau

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 le...