Landslides pose a serious risk to human life and the natural environment. Here, we compare
machine learning algorithms including the generalized linear model (GLM), mixture …

Landslides are a dangerous natural hazard that can critically harm road infrastructure in
mountainous places, resulting in significant damage and fatalities. The primary purpose of …

Landslides are one of the most devastating natural hazards causing huge loss of life and
damage to properties and infrastructures and adversely affecting the socioeconomy of the …

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

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 performs a GIS-based landslide susceptibility assessment using a convolutional
neural network, CNN, in a study area of the Gorzineh-khil region, northeastern Iran. For this …

Landslide susceptibility study is a critically important topic throughout the globe owing to the
social and financial catastrophes of landslides. The present research aims to evaluate as …

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

The prediction of landslide is a complex task but preparing the landslide susceptibility map
through artificial intelligence approaches can reduce life loss and damages resulting from …

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 …