Agricultural Non-Point Source Pollution Risk Modeling
Supported by
This project identifies spatial and temporal patterns of nutrient-related water-quality risk — combining geospatial analysis, hydrologic modeling, and machine learning — to support more effective watershed management under land-use and climate change.
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)