Landslide susceptibility map** (LSM) has been widely used as an important reference for
development and construction planning to mitigate the potential social‐eco impact caused …

In this paper, we do a critical review of statistical methods for landslide susceptibility
modelling and associated terrain zonations. Landslide susceptibility is the likelihood of a …

Abstract “Spatial contraindication” is what exactly landslide susceptibility models have been
seeking. They are designed for depicting perilous land activities, be it natural or …

In recent years, landslide susceptibility map** has substantially improved with advances
in machine learning. However, there are still challenges remain in landslide map** due to …

A new work-flow is proposed to unify the way the community shares Logistic Regression
results for landslide susceptibility purposes. Although Logistic Regression models and …

Landslides as major geo-hazards in Sweden adversely impact on nearby environments and
socio-economics. In this paper, a landslide susceptibility map using a proposed subdivision …

Gully erosion constitutes a serious problem for land degradation in a wide range of
environments. The main objective of this research was to compare the performance of seven …

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 to …

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