Large-scale flood modeling and forecasting are enhanced by the FloodCast framework, integrating multi-satellite observations and a geometry-adaptive physics-informed neural solver.
Developing FloodCast for large-scale flood modeling and forecasting, integrating multi-satellite observations and hydrodynamic modeling.
Ensemble learning improves probabilistic predictions in spatial interpolation.
The author proposes a physics-constrained deep learning framework to model soil moisture dynamics and assess the impact of different optimization strategies on the accuracy of predictions.
Physics-aware machine learning integrates physical principles into ML, revolutionizing hydrology by bridging process-based hydrology and ML.