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 …

To perform landslide susceptibility prediction (LSP), it is important to select appropriate
map** unit and landslide-related conditioning factors. The efficient and automatic multi …

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

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 the existing landslide susceptibility prediction (LSP) models, the influences of random
errors in landslide conditioning factors on LSP are not considered, instead the original …

The selection of non-landslide samples has a great impact on the machine learning
modelling for landslide susceptibility prediction (LSP). This study presents a novel …

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 to …

The determination of map** units, including grid, slope, unique condition, administrative
division, and watershed units, is a very important modeling basis for landslide assessments …

This paper describes a novel methodology where Machine Learning Algorithms (MLAs)
have been integrated to assess the landslide risk for slow moving mass movements …