There have been many algorithms created and introduced in the literature inspired by
various events observable in nature, such as evolutionary phenomena, the actions of social …

Integrating powerful machine learning models with flood risk assessment and determining
the potential mechanism between risk and the driving factors are crucial for improving flood …

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 …

Flood risk assessment is a key step in flood management and mitigation, and flood risk
maps provide a quantitative measure of flood risk. Therefore, integration of deep learning …

In mountainous regions, landslides and flash floods are common and dangerous natural
hazards. They often happen at the same time and seriously affect residential areas and …

Assessing flood risk is challenging due to complex interactions among flood susceptibility,
hazard, exposure, and vulnerability parameters. This study presents a novel flood risk …

Fluvial floods endure as one of the most catastrophic weather-induced disasters worldwide,
leading to numerous fatalities each year and significantly impacting socio-economic …

Flash floods are recognized as some of the most devastating natural disasters globally,
causing significant damage to socio-economic infrastructures, ecosystems, and human lives …

The present study intends to improve the robustness of a flood susceptibility (FS) model with
a small number of parameters in data-scarce areas, such as northwest Bangladesh, by …

This paper presents a novel artificial intelligence model to automatically recognize concrete
spall appearing on building components. The model is constructed by integrating a …