In this paper, we do a critical review of statistical methods for landslide susceptibility
modelling and associated terrain zonations. Landslide susceptibility is the likelihood of a …

Warming of the Earth climate system is unequivocal. That climate changes affect the stability
of natural and engineered slopes and have consequences on landslides, is also …

Gully erosion is identified as an important sediment source in a range of environments and
plays a conclusive role in redistribution of eroded soils on a slope. Hence, addressing …

The aim of the current study is to map landslide susceptibility over the Ziarat watershed in
the Golestan Province, Iran, using Maximum Entropy (ME), as a machine learning model …

Abstract High Mountain Asia is impacted by extreme monsoonal rainfall that triggers
landslides in large proportions relative to global distributions, resulting in substantial human …

Global warming is exacerbating weather, and climate extremes events and is projected to
aggravate multi-sectorial risks. A multiplicity of climate hazards will be involved, triggering …

A landslide susceptibility analysis has been made in the Río Aguas catchment (Almeria,
Southeast Spain), using two statistical models, Maximum Entropy (MaxEnt) and …

Abstract Machine learning models are gradually replacing traditional techniques used for
landslide susceptibility assessment. This study aims to comprehensively compare multiple …

Landslides are frequent and widespread geomorphological phenomena causing loss of
human life and damage to property. The main tool for assessing landslide risk relies on …

The purpose of this study is to identify the key factors that influence the availability of
groundwater resources in a 10-year period in the Gonabad region of Iran using a maximum …