The spatial heterogeneity of landslide influencing factors is the main reason for the poor
generalizability of the susceptibility evaluation model. This study aimed to construct a …

Landslides are one of the most important environmental hazards occur naturally or human-
induced with large-scale social, economic, and environmental impacts. Landslide …

There is a growing demand for detailed and accurate landslide maps and inventories
around the globe, but particularly in hazard-prone regions such as the Himalayas. Most …

The main objective of the current study was to introduce a Deep Learning Neural Network
(DLNN) model in landslide susceptibility assessments and compare its predictive …

Landslides are highly hazardous geological disasters that can potentially threaten the safety
of human life and property. As a result, landslide susceptibility mapping (LSM) plays an …

Globally, but especially in China, landslides are considered to be one of the most severe
and significant natural hazards. In this study, bivariate statistical-based kernel logistic …

The current study aimed at evaluating the capabilities of seven advanced machine learning
techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF) …

The machine-learning “black box” models, which lack interpretability, have limited
application in landslide susceptibility mapping. To interpret the black-box models, some …

The main aim of the present study is to explore and compare three state-of-the art data
mining techniques, best-first decision tree, random forest, and naïve Bayes tree, for landslide …

Landslides are regarded as one of the most common geological hazards in a wide range of
geo-environment. The aim of this study is to assess landslide susceptibility by integrating …