Coupling machine learning algorithms with spatial analytical techniques for landslide
susceptibility modeling is a worth considering issue. So, the current research intend to …

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

This paper evaluates a comparison between three machine learning algorithms (MLAs),
namely support vector machine (SVM), multilayer perceptron artificial neural network (MLP …

The concept of leveraging the predictive capacity of predisposing factors for landslide
susceptibility (LS) modeling has been continuously improved in recent work focusing on …

Landslides are natural and often quasi-normal threats that destroy natural resources and
may lead to a persistent loss of human life. Therefore, the preparation of landslide …

Detecting and mapping landslides are crucial for effective risk management and planning.
With the great progress achieved in applying optimized and hybrid methods, it is necessary …

Landslide risk prevention requires the delineation of landslide-prone areas as accurately as
possible. Therefore, selecting a method or a technique that is capable of providing the …

The main aim of this study is to use weights of evidence (WoE), logistic regression (LR),
naïve Bayes (NB), and alternating decision tree (ADTree) models to draw a landslide …

Commonly used data-driven models for landslide susceptibility prediction (LSP) can be
mainly classified as heuristic, general statistical or machine learning models. This study …

This analysis aims to generate landslide susceptibility maps (LSMs) using various machine
learning methods, namely random forest (RF), alternative decision tree (ADTree) and …