With the increasing threat of recurring landslides, susceptibility maps are expected to play a
bigger role in promoting our understanding of future landslides and their magnitude. This …

Flood risk mapping and modeling is important to prevent urban flood damage. In this study,
a flood risk map was produced with limited hydrological and hydraulic data using two state …

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

Flood is one of the most destructive natural disasters which cause great financial and life
losses per year. Therefore, producing susceptibility maps for flood management are …

This paper aims to use recurrent neural networks (RNNs) to perform landslide susceptibility
mapping in Yongxin County, China. The two main contributions of this study are summarized …

The main objective of the present study was to produce a novel ensemble data mining
technique that involves an adaptive neuro-fuzzy inference system (ANFIS) optimized by …

In recent years, landslide susceptibility mapping has substantially improved with advances
in machine learning. However, there are still challenges remain in landslide mapping due to …

Urban flood risk mapping plays a decisive role in urban management and planning,
especially in reducing flood damages. In this study, a flood risk map was produced for …

The spatial prediction of landslide susceptibility is an important prerequisite for the analysis
of landslide hazards and risks in any area. This research uses three data mining techniques …

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 …