In this study, we developed multiple hybrid machine-learning models to address parameter
optimization limitations and enhance the spatial prediction of landslide susceptibility models …

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

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 …

The objective of this investigation is to produce maps identifying areas prone to landslides
(LSMs) by utilizing multiple machine learning techniques, including the harmony search …

This paper aims to apply and compare the performance of the three machine learning
algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and …

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

This study compares the predictive performance of GIS-based landslide susceptibility
mapping (LSM) using four different kernel functions in support vector machines (SVMs) …

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

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 …

This study propose a new approach through which the landslide susceptibility in Quang
Nam (Vietnam) will be estimated using the best model among the following algorithms …