The results show that the value of physics-informed learning depends on whether the constrained physical process is resolved at the modelling timestep, and suggest that temporal resolution should be considered explicitly when designing and evaluating physics-informed hydrological models.
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...
Data-driven streamflow prediction models have achieved high predictive accuracy, but the relative contributions of meteorological forcing, hydrologic memory, and spatial watershed connectivity remain poorly understood, particularly in cold-region watersheds. This study proposes a six-tier diagnostic framework to quanti...
A. Ajao, Y. Lim· Water resources management· 0 citations
Dynamic prediction of reservoir water cut supports development-plan optimization and injection-production control. The 3000 scenarios sampled the reservoir-production-control response space, and the surrogate model learned the mapping from input conditions to dynamic responses for rapid prediction of unseen scenarios....
The results demonstrate that deep learning models, particularly when enriched with spatially interpolated inputs and land cover variability, significantly enhance forecasting accuracy and provide valuable projections for future water availability but also informs sustainable water management strategies in climate-sensi...
Ayse Ozer, A. Shahbazi, Halit Apaydın· Water resources management· 0 citations
