This study aims to examine three machine learning (ML) techniques, namely random forest
(RF), LightGBM, and CatBoost for flooding susceptibility maps (FSMs) in the Vietnamese Vu …

Vietnam's central coastal region is the most vulnerable and always at flood risk, severely
affecting people's livelihoods and socio-economic development. In particular, Quang Binh …

Improving the accuracy of flood prediction and mapping is crucial for reducing damage
resulting from flood events. In this study, we proposed and validated three ensemble models …

Globally, many studies on machine learning (ML)-based flood susceptibility modeling have
been carried out in recent years. While majority of those models produce reasonably …

Flooding is one of the most destructive natural hazards that has caused catastrophic effects
worldwide. Recently, machine learning methods have become widespread in flood …

Floods are one of nature's most destructive disasters because of the immense damage to
land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to …

Recently, floods are occurring more frequently every year around the world due to increased
anthropogenic activities and climate change. There is a need to develop accurate models for …

The objective of this study was the development of an approach based on machine learning
and GIS, namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Gradient-Based …

Floods is a natural hazard that occurs over a short time with a high transmission speed.
Flood risk management in many countries employs flood susceptibility modeling to mitigate …

Due to the physical processes of floods, the use of data-driven machine learning (ML)
models is a cost-efficient approach to flood modeling. The innovation of the current study …