Landslide susceptibility mapping (LSM) has been widely used as an important reference for
development and construction planning to mitigate the potential social‐eco impact caused …

This work aims to discuss and compare the inherent essence of different machine learning
algorithms for landslide susceptibility models (LSMs), which is of great significance for …

Landslide is a devastating natural disaster, causing loss of life and property. It is likely to
occur more frequently due to increasing urbanization, deforestation, and climate change …

Landslides are highly hazardous geological disasters that can potentially threaten the safety
of human life and property. As a result, landslide susceptibility mapping (LSM) plays an …

Assessments of landslide disasters are becoming increasingly urgent. The aim of this study
is to investigate a convolutional neural network (CNN) framework for landslide susceptibility …

Abstract Machine learning (ML)-based landslide susceptibility mapping (LSM) has achieved
substantial success in landslide risk management applications. However, the complexity of …

Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of
landslide occurrence, providing a scientific basis for natural hazards prevention, land use …

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

Accurate and timely landslide susceptibility mapping (LSM) is essential to effectively reduce
the risk of landslide. In recent years, deep learning has been successfully applied to …

Integration of different models may improve the performance of landslide susceptibility
assessment, but few studies have tested it. The present study aims at exploring the way to …