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

In this study, Logistics Regression (LR), Support Vector Machine (SVM), Random Forest
(RF), Gradient Boosting Machine (GBM), and Multilayer Perceptron (MLP) machine learning …

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 …

In this study, an expert knowledge-based model, a logistic regression model, and an artificial
neural network model were compared for their accuracy and portability in landslide …

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

The main purpose of the present study is to use three state-of-the-art data mining
techniques, namely, logistic model tree (LMT), random forest (RF), and classification and …

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 …

Landslides are a manifestation of slope instability causing different kinds of damage
affecting life and property. Therefore, high-performance-based landslide prediction models …

Landslides pose significant and serious geological threat disasters worldwide, threatening
human lives and property; China is particularly susceptible to these disasters. This paper …

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