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

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

The present study aims to develop two hybrid models to optimize the factors and enhance
the predictive ability of the landslide susceptibility models. For this, a landslide inventory …

The application of ensemble learning models has been continuously improved in recent
landslide susceptibility research, but most studies have no unified ensemble framework …

With the growth of cities, urban flooding has increasingly become an issue for regional and
national governments. The destructive effects of floods are magnified in cities. Accurate …

This study aims to develop two optimized models of landslide susceptibility map** (LSM),
ie, logical regression (LR) and random forest (RF) models, premised on hyperparameter …

The main objective of the current study was to introduce a Deep Learning Neural Network
(DLNN) model in landslide susceptibility assessments and compare its predictive …

Flood is a devastating natural hazard that may cause damage to the environment
infrastructure, and society. Hence, identifying the susceptible areas to flood is an important …

This study proposed an integrated approach to generating a forest fire risk map. It used
geographic information system–based multiple criteria decision analysis (GIS-MCDA) with …

Landslides are a common type of natural disaster that brings great threats to the human lives
and economic development around the world, especially in the Chinese Loess Plateau …