Data-driven landslide susceptibility models formally integrate spatial landslide information
with explanatory environmental variables that describe predisposing factors of slope instability…

Empirical models are frequently applied to produce landslide susceptibility maps for large
areas. Subsequent quantitative validation results are routinely used as the primary criteria to …

The increasing availability of long-term observational data can lead to the development of
innovative modelling approaches to determine landslide triggering conditions at regional scale…

There is unanimous agreement that a precise spatial representation of past landslide
occurrences is a prerequisite to produce high quality statistical landslide susceptibility models. …

Shallow landslide initiation typically results from an interplay of dynamic triggering and
preparatory conditions along with static predisposition factors. While data-driven methods for …

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 …

China is one of the countries where landslides caused the most fatalities in the last decades.
The threat that landslide disasters pose to people might even be greater in the future, due …

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, China …

In mountain basins, sediment supply to the fluvial system occurs mainly through episodic
geomorphic processes—such as debris flows and other landslide types—whose effectiveness …

Multi-temporal landslide inventories in widely forested landscapes are scarce and further
studies are required to face the challenges of producing reliable inventories in woodland areas…