In recent decades, data-driven landslide susceptibility models (DdLSM), which are based on
statistical or machine learning approaches, have become popular to estimate the relative …

Landslide susceptibility corresponds to the probability of landslide occurrence across a
given geographic space. This probability is usually estimated by using a binary classifier …

Abstract Machine learning algorithms are an important measure with which to perform
landslide susceptibility assessments, but most studies use GIS-based classification methods …

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

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

This study attempts to identify the essential conditioning factors of landslides to increase the
predictive ability of landslide susceptibility models and explore the effects of different grid …

In this study, we developed multiple hybrid machine-learning models to address parameter
optimization limitations and enhance the spatial prediction of landslide susceptibility models …

Using the physical deterministic model to analyze landslide stability has become a hotspot
of landslide disasters research all over the world. The Digital Elevation Model (DEM) …

The determination of map** units, including grid, slope, unique condition, administrative
division, and watershed units, is a very important modeling basis for landslide assessments …

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