2 papers
cs.LG2026
Improving Model Performance by Adapting the KGE Metric to Account for System Non-Stationarity
M Jawad, HV Gupta, YH Wang +3
Geoscientific systems tend to be characterized by pronounced temporal non-stationarity, arising from seasonal and climatic variability in hydrometeorological drivers, and from natu…
cs.LG2026
Process-Aware AI for Rainfall-Runoff Modeling: A Mass-Conserving Neural Framework with Hydrological Process Constraints
Mohammad A. Farmani, Hoshin V. Gupta, Ali Behrangi +3
Machine learning models can achieve high predictive accuracy in hydrological applications but often lack physical interpretability. The Mass-Conserving Perceptron (MCP) provides a…