Hybrid Modeling for Fine-Scale Runoff Prediction
- Period
- 2021–present
This research develops hybrid modeling approaches that pair process-based hydrologic models with data-driven methods to predict runoff at fine spatial scales, while keeping results physically interpretable.
The work spans edge-of-field runoff prediction, generalization of runoff-risk models, and methodological frameworks for improving data-driven model performance.
Related publications
- Assessing Hybrid Modeling for Fine-Scale Runoff Prediction (2024)
- A methodological framework for improving the performance of data-driven models (2023)
- Generalization of Runoff Risk Prediction (2022)
- Edge-of-field runoff prediction by a hybrid modeling approach (2021)
From this research area
- Street-to-pipe diagnosis of compound rain–tailwater flooding (2026)
- Hydrological extremes heighten vulnerability to schistosomiasis (2024)
- A methodological framework for improving the performance of data-driven models (2023)
- Editorial: Coastal Flooding: Modeling, Monitoring, and Protection Systems (2022)
- Generalization of Runoff Risk Prediction (2022)