10 papers
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…
On the Adversarial Robustness of Hydrological Models
Yang Yang, Joseph Janssen, Hoshin Gupta +1
The evaluation of hydrological models is essential for both model selection and reliability assessment. However, simply comparing predictions to observations is insufficient for un…
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…
Structure-Function Coherent Coarsening for Cross-Resolution Ecohydrological Modeling
Long Jiang, Yang Yang, Morgan Thornwell +2
Ecohydrological models are increasingly applied across multiple scenarios, yet their application remains constrained by high computational costs of fine-resolution simulations and…
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…