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 …

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 …

The objective of this study is to investigate a potential application of the Adaptive Neuro-
Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a …

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 …

The purpose of the present study is to compare the prediction performances of three different
approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro …

The purpose of this paper is to produce a reliable susceptibility mapping using frequency
ratio (FR), statistical index (SI), and certainty factor (CF) models with the aid of geographic …

The purpose of the present paper is to manifest the results of the neuro-fuzzy model using
remote sensing data and GIS for landslide susceptibility analysis in a part of the Klang …

Landslide susceptibility mapping is essential in delineating landslide prone areas in
mountainous regions. The primary purpose of this study is to evaluate landslide …

Landslides frequently occur along the eastern margin of the Tibetan Plateau, which poses a
risk to the construction, maintenance, and transportation of the proposed Dujiangyan city to …

The main aim of present study is to compare three GIS-based models, namely Dempster–
Shafer (DS), logistic regression (LR) and artificial neural network (ANN) models for landslide …