Landslide susceptibility maps (LSMs) rely on statistical association for weighting the impact
of each predictive factor class. However, the effects of using different training datasets and …

Previous research showed that the accuracy of landslide susceptibility maps (LSM) mainly
depends on the landslide inventory used to train the algorithms. However, the preparation of …

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 …

Landslides present a significant hazard to human life, infrastructure, and property,
particularly in mountainous regions. In Morocco, these risks have garnered increased …

In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very
important, as it helps spatially identify potential landslide-prone regions. This study used a …

Landslides are one of the catastrophic natural hazards that occur in mountainous areas,
leading to loss of life, damage to properties, and economic disruption. Landslide …

The concept of leveraging the predictive capacity of predisposing factors for landslide
susceptibility (LS) modeling has been continuously improved in recent work focusing on …

The purpose of the present study was to compare the prediction performances of three
statistical methods, namely, information value (IV), weight of evidence (WoE) and frequency …

This study aimed to examine the influence of the random selection of landslide training and
testing sets on the predictive performance of the shallow landslide susceptibility modelling at …

Improving the predictive accuracy of models based on machine learning techniques for
assessing landslide susceptibility is an area that attracts researchers' attention. In this study …