Ongoing landslides have wreaked havoc in various regions across the globe. This article
presents a study of two forms of landslide monitoring viz; creation of Landslide Susceptibility …

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

Landslide susceptibility map** is a common practice for landslide susceptibility
assessment across the world. Like many other mountainous areas of the world, Bangladesh …

This study intends to showcase the application of Extreme Gradient boosting (XGboost), a
state-of-the-art ensemble-learning technique, for district-scale mineral potential map** …

Floods are the natural disaster that occurs most frequently due to the weather and causes
the most widespread destruction. The purpose of the proposed research is to analyze flood …

Recently, one of the most frequent natural hazards around several regions in the world is the
landslide events. The area of Jabal Farasan in the northwest Jeddah of Saudi Arabia suffers …

Landslide susceptibility depends on various causal factors such as geology, land use/land
cover (LULC), slope, and elevation. Unlike other factors that are relatively stable over time …

Flood is the most common phenomenon causing extensive disruption to the environment,
socio-economy, infrastructure and many other aspects of human life. Flood susceptibility …

Flash floods pose a major challenge in various regions of the world, causing serious
damage to life and property. Here we investigated the Izvorul Dorului river basin from …

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 …