2 citations · 3 across the 10 of their papers we have counts for
8 papers · 1 filter
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 Mass-Conserving Perceptron (MCP) establishes a modeling paradigm in which conceptual hydrologic models can be reformulated as physically constrained, conceptually interpretable…
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…
KAN-Matrix: Visualizing Nonlinear Pairwise and Multivariate Contributions for Physical Insight
Luis A. De la Fuente, Hernan A. Moreno, Laura V. Alvarez +1
Interpreting complex datasets remains a major challenge for scientists, particularly due to high dimensionality and collinearity among variables. We introduce a novel application o…
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…
Knowledge distillation as a pathway toward next-generation intelligent ecohydrological modeling systems
Long Jiang, Yang Yang, Ting Fong May Chui +2
Simulating ecohydrological processes is essential for understanding complex environmental systems and guiding sustainable management amid accelerating climate change and human pres…
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…