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 …

Landslides are the most destructive geological hazard in the hilly regions. For systematic
landslide mitigation and management, landslide evaluation and hazard zonation is required …

The main objective of the current study was to introduce a Deep Learning Neural Network
(DLNN) model in landslide susceptibility assessments and compare its predictive …

Globally, but especially in China, landslides are considered to be one of the most severe
and significant natural hazards. In this study, bivariate statistical-based kernel logistic …

The application of ensemble learning models has been continuously improved in recent
landslide susceptibility research, but most studies have no unified ensemble framework …

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 …

Preparation of landslide susceptibility maps is considered as the first important step in
landslide risk assessments, but these maps are accepted as an end product that can be …

Rapid developments in Internet of Things (IoT) systems have led to a wide integration of
such systems into everyday life. Systems for active real-time monitoring are especially useful …

This paper aims to use recurrent neural networks (RNNs) to perform landslide susceptibility
mapping in Yongxin County, China. The two main contributions of this study are summarized …

Landslide susceptibility assessment of Uttarakhand area of India has been done by applying
five machine learning methods namely Support Vector Machines (SVM), Logistic …