From the 1 of 3 linked papers with an AI index.
3 papers
cs.LG2026
From Conceptual Hydrologic Models to Conceptually Interpretable Neural Networks: A Snow-Water Mass-Conserving-Perceptron Framework for Discovering Catchment-Scale Precipitation-Storage-Runoff Representations
Yuan-Heng Wang, Hoshin V. Gupta
The paper introduces a Mass-Conserving Perceptron framework that reformulates conceptual hydrologic models as physically constrained, interpretable neural networks for snow‑water a…
cs.LG2026
Towards CONUS-Wide ML-Augmented Conceptually-Interpretable Modeling of Catchment-Scale Precipitation-Storage-Runoff Dynamics
Yuan-Heng Wang, Yang Yang, Fabio Ciulla +2
While many modern studies are dedicated to ML-based large-sample hydrologic modeling, these efforts have not necessarily translated into predictive improvements that are grounded i…
cs.LG2025
Using Machine Learning to Discover Parsimonious and Physically-Interpretable Representations of Catchment-Scale Rainfall-Runoff Dynamics
Yuan-Heng Wang, Hoshin V. Gupta
Due largely to challenges associated with physical interpretability of machine learning (ML) methods, and because model interpretability is key to credibility in management applica…