Geotechnical engineering deals with soils and rocks and their use in engineering
constructions. By their nature, soils and rocks exhibit complex behaviours and a high level of …

Riverside landslides present a significant geohazard globally, posing threats to
infrastructure and human lives. In line with the United Nations' Sustainable Development …

Abstract Machine learning algorithms are an important measure with which to perform
landslide susceptibility assessments, but most studies use GIS-based classification methods …

Landslides are the most destructive geological hazard in the hilly regions. For systematic
landslide mitigation and management, landslide evaluation and hazard zonation is required …

Preparation of landslide susceptibility maps is considered as the first important step in
landslide risk assessments, but these maps are accepted as an end product that can be …

The main objective of the present study was to compare the performance of a classifier that
implements the Logistic Regression and a classifier that employs a Naïve Bayes algorithm in …

In recent decades, data-driven landslide susceptibility models (DdLSM), which are based on
statistical or machine learning approaches, have become popular to estimate the relative …

Gully erosion constitutes a serious problem for land degradation in a wide range of
environments. The main objective of this research was to compare the performance of seven …

The main objective of this study is to propose and verify a novel ensemble methodology that
could improve prediction performances of landslide susceptibility models. The proposed …

The objective of this study is to investigate and compare the results of three data mining
approaches, the support vector machines (SVM), decision tree (DT), and Naïve Bayes (NB) …