Landslide susceptibility projections that rely on independent models produce biased results.
This situation will worsen class balance if working with a small population. This study …

Landslide susceptibility assessment was adopted for the Idukki region using 6 machine
learning models viz., Adaptive Boosting (AdaBoost), Naïve Bayes (NB), Neural Network …

The landslide disaster in China is widespread and serious. Regional landslide risk
assessment has always been one of the most important contents of landslide disaster …

Landslides are one of the most devastating natural hazards causing huge loss of life and
damage to properties and infrastructures and adversely affecting the socioeconomy of the …

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 …

Data-driven model has been increasingly used in landslide susceptibility mapping (LSM).
However, the traditional landslide susceptibility assessment lacks reasonable and effective …

The present study is aimed at conducting a comparative landslide susceptibility assessment
in a landslide-prone subset area of the Tajan Watershed in northern Iran. For this aim, three …

Identifying the uncertainty caused by the number of non-landslides is necessary to obtain a
precise landslide susceptibility map. Hence, the objective of this study is to investigate the …

Owing to the difficulties in determining the boundaries of landslides from landslide inventory,
landslide samples are often expressed by geometrical points, rather than the actual shape of …

The purpose of this study is to utilize three machine learning models—random forest, logistic
regression and extreme gradient boosting—to assess the landslide susceptibility of Wushan …