The most dangerous landslide disasters always cause serious economic losses and human
deaths. The contribution of this work is to present an integrated landslide modelling …

This paper presents GIS-based new ensemble data mining techniques that involve an
adaptive neuro-fuzzy inference system (ANGIS) with genetic algorithm, differential evolution …

Landslides are natural and sometimes quasi-natural hazards that are destructive to natural
resources and cause loss of human life every year. Hence, preparing susceptibility maps for …

As threats of landslide hazards have become gradually more severe in recent decades,
studies on landslide prevention and mitigation have attracted widespread attention in …

In this research, landslide susceptibility map of the Fahliyan sub-basin was provided
employing adaptive neuro-fuzzy inference system (ANFIS) in ensemble with the ant colony …

Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle
swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) …

The main purpose of this study was to apply the novel bivariate weights-of-evidence-based
SysFor (SF) for landslide susceptibility mapping, and two machine learning techniques …

The main objective of the present study was to produce a novel ensemble data mining
technique that involves an adaptive neuro-fuzzy inference system (ANFIS) optimized by …

The main aim of the present study is to explore and compare three state-of-the art data
mining techniques, best-first decision tree, random forest, and naïve Bayes tree, for landslide …

The spatial prediction of landslide susceptibility is an important prerequisite for the analysis
of landslide hazards and risks in any area. This research uses three data mining techniques …