Over the past few years, Deep Learning (DL) methods have garnered substantial
recognition within the field of hydrology and water resources applications. Beginning with a …

Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …

Urban flooding risks, often overlooked by conventional methods, can be profoundly affected
by city configurations. However, explainable Artificial Intelligence could provide insights into …

Accurate assessment of soil water erosion (SWE) susceptibility is critical for reducing land
degradation and soil loss, and for mitigating the negative impacts of erosion on ecosystem …

Clean water is an indispensable essential resource on which humans and other living
beings depend. Therefore, the establishment of a water quality prediction model to predict …

Global climate change and rapid urbanization, mainly driven by anthropogenic activities,
lead to urban flood vulnerability and uncertainty in sustainable stormwater management …

Streamflow forecasting is critical for real-time water resources management and flood early
warning. In this study, we introduce a novel attention-based Long-Short Term Memory …

Develo** accurate lake water level (WL) forecasting models is important for flood control,
shoreline maintenance and sustainable water resources planning and management. In this …

Urban pluvial flooding has become a threatening hazard to ecosystem and human lives in
recent years. Identifying its driving factors is essential for stormwater management. A …

With catastrophic climate change and accelerated urbanization, urban flooding has
emerged as the most influential hazard over last few decades. Therefore, a systematic study …