2 citations · 3 across the 10 of their papers we have counts for
10 papers
Effective catchment hydrologic representations are low-dimensional but interaction-dependent
Yuan-Heng Wang, Hoshin V. Gupta
Hydrologic models must reconcile predictive flexibility with physical interpretability, yet the complexity required to reproduce catchment dynamics remains uncertain. Here we compa…
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 +5
Ecohydrological models are increasingly applied in regional scenario-based studies, yet their cross-resolution application remains constrained by high computational costs of fine-r…
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…
Towards Interpretable Physical-Conceptual Catchment-Scale Hydrological Modeling using the Mass-Conserving-Perceptron
Yuan-Heng Wang, Hoshin V. Gupta
We investigate the applicability of machine learning technologies to the development of parsimonious, interpretable, catchment-scale hydrologic models using directed-graph architec…
A Mass-Conserving-Perceptron for Machine Learning-Based Modeling of Geoscientific Systems
Yuan-Heng Wang, Hoshin V. Gupta
Although decades of effort have been devoted to building Physical-Conceptual (PC) models for predicting the time-series evolution of geoscientific systems, recent work shows that M…