2 citations · 3 across the 5 of their papers we have counts for
5 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…
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…
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…