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Machine-Learning Surrogates for Hydrologic Prediction

These surrogate models emulate WRF-Hydro streamflow outputs to speed up scenario testing and support explainable analysis of climate, land-use, and hydrologic controls.

Related publications

  • Street-to-pipe diagnosis of compound rain–tailwater flooding (2026)
  • Assessing Hybrid Modeling for Fine-Scale Runoff Prediction (2024)
  • Hydrological extremes heighten vulnerability to schistosomiasis (2024)
  • A methodological framework for improving the performance of data-driven models (2023)
  • Coastal Flooding: Modeling, Monitoring, and Protection Systems (2022)
  • Generalization of Runoff Risk Prediction (2022)