Landslides are abundant in mountainous regions. They are responsible for substantial
damages and losses in those areas. The A1 Highway, which is an important road in Algeria …

As threats of landslide hazards have become gradually more severe in recent decades,
studies on landslide prevention and mitigation have attracted widespread attention in …

In recent years, landslide susceptibility mapping has substantially improved with advances
in machine learning. However, there are still challenges remain in landslide mapping due to …

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 …

Landslide susceptibility maps provide crucial information that helps local authorities, public
institutions, and land-use planners make the correct decisions when they are managing …

This study aims to evaluate risk and discover the distribution law for landslides, so as to
enrich landslide prevention theory and method. It first selected Fengjie County in the Three …

Identifying the uncertainty caused by the number of non-landslides is necessary to obtain a
precise landslide susceptibility map. Hence, the objective of this study is to investigate the …

Landslide susceptibility assessment is the globally approved procedure to prepare geo-
hazard maps of landslide-prone areas, which are highly used in urban management and …

In this study, the performances of machine learning models, such as artificial neural
networks (ANN), gradient-boosting machines (GBM), random forest (RF) and support vector …

Landslide susceptibility modeling, an essential approach to mitigate natural disasters, has
witnessed considerable improvement following advances in machine learning (ML) …