Artificial intelligence (AI) holds significant promise for advancing natural disaster
management through the use of predictive models that analyze extensive datasets, identify …

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

Accurate runoff forecasting plays a vital role in issuing timely flood warnings. Whereas,
previous research has primarily focused on historical runoff and precipitation variability …

Predicting landslides is becoming a crucial global challenge for sustainable development in
mountainous areas. This research compares the landslide susceptibility maps (LSMs) …

Disasters can have devastating impacts on communities and economies, underscoring the
urgent need for effective strategic disaster risk management (DRM). Although Artificial …

This study examines the use of snow avalanche susceptibility maps (SASMs) to identify
areas prone to avalanches and develop measures to mitigate the risk in the Province of …

Landslide susceptibility mapping (LSM) research is vital for averting and mitigating regional
landslide disasters. Nevertheless, there has been a lack of systematic analysis regarding …

The accurate prediction of landslide susceptibility relies on effectively handling landslide
absence samples in machine learning (ML) models. However, existing research tends to …

With the development of landslide monitoring system, many attempts have been made to
predict landslide failure-time utilizing monitoring data of displacements. Classical models …

Artificial intelligence and machine learning have attracted significant attention in the
preparation of landslide susceptibility maps (LSMs) over the years. Achieving considerable …