Fully supervised machine learning models are widely applied for landslide susceptibility
prediction (LSP), mainly using landslide and non-landslide samples as output variables and …

Landslides represent one of the world's most dangerous and widespread risks, annually
causing thousands of deaths and billions of dollars worth of damage. Building on and …

To perform landslide susceptibility prediction (LSP), it is important to select appropriate
mapping unit and landslide-related conditioning factors. The efficient and automatic multi …

To develop a better spatial prediction model of landslide susceptibility along mountain
highways, this study compared assessment models of landslide susceptibility along …

The aim of this study was to develop a hybrid model (Geo-RFE-RF) for Landslide
Susceptibility Mapping (LSM) predicated on GeoDetector and Random Forest (RF) using the …

For linear conditioning factors such as rivers, roads, and geological faults, existing studies
mainly use buffer analysis in Geographic Information System to obtain discrete variables …

Landslides are widely distributed worldwide and often result in tremendous casualties and
economic losses, especially in the Loess Plateau of China. Taking Wuqi County in the …

Landslides are one of the nature hazards causing a lot of casualties and property losses in
the world. Over the last decades, many researchers have made contributions in landslide …

A large number of landslides have occurred in the upstream reaches of the Jinsha River,
Tibetan Plateau due to the intensity of tectonic movement in the area. Remote sensing and …

This research aims to generate a landslide susceptibility map (LSM) for the Bhagirathi river
basin located in the Tehri Garhwal district of Uttarakhand state in India. For this study, we …