This paper demonstrates that an integrated approach to uncertainty quantification enhances both the prediction accuracy and the explainability of ensemble streamflow 20 predictions, and provides a foundation for “uncertainty - aware” deep learning -based streamflow prediction.
Deep learning has improved precipitation estimation and forecasting, but high predictive skill does not by itself ensure physically credible behavior, robustness under distribution shift, or reliable uncertainty. This critical narrative review examines physics-informed deep learning across quantitative precipitation es...
Hao Yang, Yan-Ni Wang, Min Chen et al.· Atmosphere· 0 citations
Coastal aquifers are increasingly vulnerable to seawater intrusion, climate change, and intensive groundwater extraction, while available monitoring data are often limited in both quality and quantity. These constraints restrict the application of physics-based models and complicate reliable risk assessment and managem...
Ming Cheng, Matilde Niccolucci, Lupicinio García Ortiz et al.· Stochastic environmental res...· 0 citations
The analysis reveals a transition from deterministic DI models to hybrid, physics-informed, uncertainty-aware, and operational forecasting systems that increasingly integrate AI with physical knowledge and heterogeneous environmental observations.
Braiton U. Mukhalela, S. Viriri, D. Ndzi et al.· Frontiers in Artificial Inte...· 0 citations
Rainfall-runoff (R-R) forecasting in data-scarce regions remains challenging due to limited hydrometeorological observations, uncertainties in satellite-based precipitation products (SPPs), and the inability of individual modeling approaches to fully represent complex watershed dynamics. To address these challenges...
A. Molajou, Alireza Tajbakhsh, Mehrdad Shokatian-Beiragh et al.· Scientific Reports· 0 citations
Flood-prone Bangladesh requires accurate short-term rainfall prediction, yet conventional numerical weather prediction (NWP) models remain computationally intensive and regionally limited in skill. This study evaluates and compares four deep learning (DL) architectures — Simple Long Short-Term Memory (LSTM), Stacked LS...
Flooding remains one of the most damaging natural hazards worldwide, and the growing availability of hydrological sensor networks and satellite data has pushed researchers toward data-driven alternatives to conventional physics-based hydrological models. This paper synthesizes findings from six recent bodies of work —...
Chetan Pathe, Yash Borkar, Purva Waghmare et al.· International Journal of Cre...· 0 citations
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