Landslide occurrence has become increasingly influenced by human activities. Accordingly,
changing land use and land cover (LULC) is an important conditioning factor in landslide …

Upon the introduction of machine learning (ML) and its variants, in the form that we know
today, to the landslide community, many studies have been carried out to explore the …

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

This research presents the results of the GIS-based statistical models for generation of
landslide susceptibility map** using geographic information system (GIS) and remote …

The aim of this work is to define reliable susceptibility models for shallow landslides using
Logistic Regression and Random Forests multivariate statistical techniques. The study area …

The main goal of this study is to produce landslide susceptibility maps of a landslide-prone
area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) …

Landslides are one of the most important environmental hazards occur naturally or human-
induced with large-scale social, economic, and environmental impacts. Landslide …

In many parts of the world, landslide susceptibility remains inadequately mapped, due to the
lack of both data and suitable methods for widespread implementation. Iran is one of those …

As a first step forward in regional hazard management, multivariate statistical analysis in the
form of logistic regression was used to produce a landslide susceptibility map in the Kakuda …

Every year, the Republic of Korea experiences numerous landslides, resulting in property
damage and casualties. This study compared the abilities of frequency ratio (FR), analytic …