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 …

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

Reservoir landslides pose a great threat to shipping safety, human lives and properties, and
the operation of the hydropower station. In this paper, the 24 June 2015 Hongyanzi …

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

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 …

Abstract Background High basin of Oum Er Rbia River, which is located in Middle Atlas
Mountain region, is prone to landslide problems due to the geological features combined …

Uatzau basin in northwestern Ethiopia is one of the most landslide-prone regions, which
characterized by frequent high landslide occurrences causing damages in farmlands, non …

Abstract The Bostanlik district, Uzbekistan, is characterized by mountainous terrain
susceptible to landslides. The present study aims at creating a statistically derived landslide …

Landslide monitoring is a mandatory step in landslide risk assessment. It requires collecting
data on landslide conditions (eg, areal extent, landslide kinematics, surface topography …

Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention
and mitigation. However, delineating the spatial occurrence pattern of the landslide remains …