Floods, as a catastrophic phenomenon, have a profound impact on ecosystems and human
life. Modeling flood susceptibility in watersheds and reducing the damages caused by …

The current study aimed at evaluating the capabilities of seven advanced machine learning
techniques (MLTs), including, Support Vector Machine (SVM), Random Forest (RF) …

This study aims to evaluate landslide causative factors in landslide susceptibility
assessments and to compare landslide susceptibility models based on the bivariate …

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 …

Landslide is a natural hazard that results in many economic damages and human losses
every year. Numerous researchers have studied landslide susceptibility mapping (LSM) …

Abstract Study region Guna Tana Landscape, Upper Blue Nile Basin, Ethiopia. Study focus
This paper aimed to delineate the groundwater potential zones using GIS and remote …

Flood has long been known as one of the most catastrophic natural hazards worldwide.
Mapping flood-prone areas is an important part of flood disaster management. In this study …

The purpose of the current study is to produce landslide susceptibility maps using different
data mining models. Four modeling techniques, namely random forest (RF), boosted …

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

Flood is one of the most prevalent natural disasters that frequently occur in the northern part
of Iran reported in hot spots of flood occurrences. The main aim of the current study was to …