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 …

Flooding is one of the most common natural hazards that have extremely detrimental
consequences. Understanding which areas are vulnerable to flooding is crucial to …

Climate change and increasing urbanization are two primary factors responsible for the
increased risk of serious flooding around the world. The prediction and monitoring of the …

Study region Major urban areas in Henan Province of central China. Study focus data fusion
technology is also a key and difficult point in the field of flood research. Remote sensing and …

The increasing availability of hydrological and physiographic spatiotemporal data has
boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high …

Flood prediction is an important task, which helps local decision-makers in taking effective
measures to reduce damage to the people and economy. Currently, most studies use …

Flood damage is becoming increasingly severe in the context of climate change and
changes in land use. Assessing the effects of these changes on floods is important, to help …

To remove vegetation bias (VB) from the global DEMs (GDEMs), an artificial neural network
(ANN)-based method with the consideration of elevation spatial autocorrelation is …

The breathtaking economic development put a heavy toll on ecology, especially on water
pollution. Efficient water resource management has a long-term influence on the sustainable …

Flood models based on traditional hydrodynamic modeling encounter significant difficulties
with real-time predictions, require enormous computational resources, and perform poorly in …