Landslide susceptibility assessment (LSA) based on machine learning methods has been
widely used in landslide geological hazard management and research. However, the …

Abstract Machine learning models have been widely used for landslide susceptibility
assessment (LSA) in recent years. The accuracy of machine learning-based LSA often …

In recent years, numerous landslide susceptibility assessment studies using conventional
machine learning models have been carried out, and a series of achievements have been …

This paper presents a novel machine learning approach backed by ensembling machine
learning algorithms to build landslide susceptibility maps. The results reveal that this …

The main objectives of the study was to apply a Logistic Regression and a Random Forest
model for the construction of a landslide susceptibility map in the Wuyuan area, China, and …

In recent years, several catastrophic landslide events have been observed throughout the
globe, threatening to lives and infrastructures. To minimize the impact of landslides, the …

This study aims to investigate the application of a class-weighted algorithm combined with
conventional machine learning model (logistic regression (LR)) and ensemble machine …

The prediction performance of conventional landslide susceptibility (LS) models is generally
limited by the size of samples in the landslide inventory, as unsatisfying performance may be …

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 …

Landslide susceptibility assessment serves as a critical scientific reference for geohazard
control, land use, and sustainable development planning. The existing research has not fully …