Upon the introduction of machine learning (ML) and its variants, in the form that we know
today, to the landslide community, many studies have been carried out to explore the …

Landslides are geomorphological processes that shape the landscapes of all continents,
dismantling mountains and contributing sediments to the river networks. Caused by …

Rainfall triggered landslides occur in all mountain ranges posing threats to people and the
environment. Given the projected climate changes, the risk posed by landslides is expected …

Due to the similarity of conditioning factors, the aggregation feature of landslides and the
multi-temporal landslide inventory, the spatial and temporal effects of landslides need to be …

Recent advances in the diversity and systematization of design methods and real-time data
have led to a general elevation in spatio-temporal accuracy for regional landslide early …

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 …

For decades, the distinction between statistical models and machine learning ones has
been clear. The former are optimized to produce interpretable results, whereas the latter …

Landslide susceptibility corresponds to the probability of landslide occurrence across a
given geographic space. This probability is usually estimated by using a binary classifier …