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 …

Geographic Information tools (GI tools) have become an essential component of research in
landscape ecology. In this article we review the use of GIS (Geographic Information …

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

The System for Automated Geoscientific Analyses (SAGA) is an open source geographic
information system (GIS), mainly licensed under the GNU General Public License. Since its …

Coupling machine learning algorithms with spatial analytical techniques for landslide
susceptibility modeling is a worth considering issue. So, the current research intend to …

This paper presents GIS-based new ensemble data mining techniques that involve an
adaptive neuro-fuzzy inference system (ANGIS) with genetic algorithm, differential evolution …

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use
and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial …

This paper introduces the current machine learning approach to solving spatial modeling
problems in the domain of landslide susceptibility assessment. The latter is introduced as a …

Forests are living dynamic systems and these unique ecosystems are essential for life on
earth. Forest fires are one of the major environmental concerns, economic, and social in the …

For decades, the distinction between statistical models and machine learning ones has
been clear. The former are optimized to produce interpretable results, whereas the latter …