Process-based modelling offers interpretability and physical consistency in many domains of
geosciences but struggles to leverage large datasets efficiently. Machine-learning methods …

Deep learning has emerged as a powerful tool for streamflow forecasting and its
applications have garnered significant interest in the hydrological community. Despite the …

In response to growing concerns surrounding the relationship between climate change and
escalating flood risk, there is an increasing urgency to develop precise and rapid flood …

Deep Learning (DL) methods have gained significant recognition in hydrology and water
resources applications in recent years. Beginning with a discussion on fundamental …

Precise assessment of suspended sediment load (SSL) is vital for many applications in
hydrological modeling and hydraulic engineering. In this study, a smoothed long short-term …

Abstract In recent years, Machine Learning (ML) techniques have gained the attention of the
hydrological community for their better predictive skills. Specifically, ML models are widely …

Streamflow is one of the key variables that fulfils various anthropogenic as well as natural
functions. Accurate prediction of streamflow poses significant challenges, especially during …

The occurrence of flash floods in urban catchments within the Mediterranean climate zone
has witnessed a substantial rise due to climate change, underscoring the urgent need for …

Streamflow prediction (SFP) constitutes a fundamental basis for reliable drought and flood
forecasting, optimal reservoir management, and equitable water allocation. Despite …

Snow is a critical component of global climate and provides water resources to over 1 billion
people worldwide. Yet current measurement methods and modeling techniques lack the …