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 …

Most literature related to landslide susceptibility prediction only considers a single type of
landslide, such as colluvial landslide, rock fall or debris flow, rather than different landslide …

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 …

Current machine learning approaches to landslide susceptibility modeling often involve
grading conditioning factors, a method characterized by substantial subjectivity and …

China has been subject to rapid urban expansion and afforestation since the economic
reform in 1978. However, the influence of land use and cover changes (LUCCs) and human …

Precipitation is the main factor that triggers landslides. Rainfall-induced landslide
susceptibility mapping (LSM) is crucial for disaster prevention and disaster losses mitigation …

Climate change can alter the frequency and intensity of extreme rainfall across the globe,
leading to changes in hazards posed by rainfall-induced landslides. In recent decades …

Landslides pose a constant threat to the lives and property of mountain people and may also
cause geomorphological destruction such as soil and water loss, vegetation destruction, and …

Abstract Motivated by the Paris Agreement, this study aims to investigate the changes in
precipitation extremes across South Asia and its five climatic zones for each 0.5° C of …