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

Optimisation plays a key role in the application of machine learning in the spatial prediction
of landslides. The common practice in optimising landslide prediction models is to search for …

Landslide poses severe threats to the natural landscape of the Lesser Himalayas and the
lives and economy of the communities residing in that mountainous topography. This study …

Landslide susceptibility assessment is crucial for land use planning, infrastructure
development, and hazard mitigation, particularly in tectonically active regions where …

Landslidesusceptibility mapping (LSM) is the primary link of geological disaster risk
evaluation, which is significant for postearthquake emergency response and rebuilding after …

Landslide susceptibility mapping (LSM) is significant for disaster prevention and mitigation,
land use management, and as a reference for decision-making. Convolutional neural …

The purpose of this study is to develop a landslide susceptibility prediction model by
applying the Frequency Ratio (FR) model and remote sensing data sets for the Northern part …

Landslide is a destructive natural hazard that causes severe property loss and loss of lives.
Numerous researchers have developed landslide susceptibility maps in order to forecast its …

Two main problems in landslide spatial prediction research are the lack of landslide
samples (minority) to train the models and the misunderstanding of assigning equal costs to …

This paper aims to compare and develop the influence on different sample sizes and sample
ratios when using machine learning (ML) models, ie, support vector machine (SVM) and …