Landslides are one of the most devastating and recurring natural disasters and have
affected several mountainous regions across the globe. The Indian Himalayan region is no …

Predicting landslides is becoming a crucial global challenge for sustainable development in
mountainous areas. This research compares the landslide susceptibility maps (LSMs) …

Using multiple ensemble learning techniques for improving the predictive accuracy of
landslide models is an active research area. In this study, we combined a radial basis …

Abstract The Sundarban Biosphere Reserve (SBR), which is one of the important coastal
regions of India, is vulnerable to storm surge hazards. It experiences storm surge flood of …

The Kosi megafan region of eastern Bihar, India, comprising of eight districts, is regularly
afflicted by large floods that cause extensive damage. Map** the possible inundation …

Landslide susceptibility maps provide crucial information that helps local authorities, public
institutions, and land-use planners make the correct decisions when they are managing …

In this study, we have developed five spatially explicit ensemble predictive machine learning
models for the landslide susceptibility map** of the Van Chan district of the Yen Bai …

In this study, Random SubSpace-based classification and regression tree (RSCART) was
introduced for landslide susceptibility modeling, and CART model and logistic regression …

The Chongqing section of the Three Gorges Reservoir area (TGRA) is a high-risk geological
catastrophe warning zone in the Yangtze River Basin. This study aimed to categorize …

Abstract In the southeastern Tibetan Plateau with complex geological conditions, the
frequent and disastrous geological hazards have posed a severe challenge to the …