Machine learning (ML) applications in hydrology are revolutionizing our understanding and
prediction of hydrological processes, driven by advancements in artificial intelligence and …

This study presents a novel approach for urban flood forecasting in drainage systems using
a dynamic ensemble-based data mining model which has yet to be utilised properly in this …

Streamflow prediction in ungauged catchments is a challenging task in hydrological studies.
Recently, data-driven models have demonstrated their superiority over traditional …

In hydrology, runoff predictions are challenging when the data is lacking (eg, predictions in
un-gauged basins (PUB) and predictions with limited data (PLD)). Here, PLD refers to the …

Flooding in urban streams can occur suddenly and cause major environmental and
infrastructure destruction. Due to the high amounts of impervious surfaces in urban …

Abstract Machine learning has been widely applied to predict the spatial or temporal
likelihood of debris flows by leveraging its powerful capability to fit nonlinear features and …

The event runoff coefficient (ie the ratio between event runoff and precipitation that
originated the runoff) is a key factor for understanding basin response to precipitation …

Prediction of dynamic environmental variables in unmonitored sites remains a long-standing
challenge for water resources science. The majority of the world's freshwater resources have …

Urban floods pose a significant threat to human communities, making its prediction essential
for comprehensive flood risk assessment and the formulation of effective resource allocation …

The storm water management model (SWMM) has been used extensively to plan,
implement, control, and evaluate low impact development facilities and other drainage …