Agent-Based Modeling of Groundwater Irrigation
- Period
- 2017–present
Project summary derived from the lab's published work — replace with the canonical project description and funding details.
This research couples agent-based models of farmer behavior with groundwater and crop systems to understand how individual irrigation and crop-choice decisions aggregate into system-scale outcomes — and how those behaviors shift over time.
It combines human and machine intelligence to derive agents' behavioral rules, and examines non-stationary irrigation behavior under changing conditions.
Related publications
- Evaluating the Impact of Non-Stationary Groundwater Irrigation Behavior (2025)
- Role of Heterogeneous Behavioral Factors in an Agent-Based Model of Crop Choice and Groundwater Irrigation (2019)
- Combining human and machine intelligence to derive agents' behavioral rules for groundwater irrigation (2017)
Related publications
- Street-to-pipe diagnosis of compound rain–tailwater flooding (2026)
- Evaluating the Impact of Non-Stationary Groundwater Irrigation Behavior (2025)
- Hydrological extremes heighten vulnerability to schistosomiasis (2024)
- Coastal Flooding: Modeling, Monitoring, and Protection Systems (2022)
- Detroit River Phosphorus Loads: Anatomy of a Binational Watershed (2019)
- Role of Heterogeneous Behavioral Factors in an Agent-Based Model of Crop Choice and Groundwater Irrigation (2019)