Case Studies
AI4WATER focuses on the use of AI optimization and prediction techniques for the water management of 4 key agricultural districts with the Mediterranean Basin. Across these sites, escalating demand, irregular rainfall, and groundwater over-extraction have lowered water tables, raised salinity, and increased pollution—especially in coastal zones. Our AI-enabled DSS turns heterogeneous data into safe, efficient, climate-robust water allocation and blending strategies.
The Case Studies:
(1) The Coastal Ras Jebel Basin (RJC) in Tunisia,
(2) The Coastal Constantinois Basin and Seybouse basin (CCB&SB) in North-East of Algeria,
(3) The Capitanata Coastal Irrigation District (CID) in Apulia region (southern Italy), and
(4) The Coastal Nile Delta Basin (NDB) in Egypt
Site type: Water-smart management pilot
System focus: Groundwater–surface water blending for irrigation & domestic use under drought and salinity stress
What we test: AI-assisted optimization (Genetic Algorithms, Reinforcement Learning, AI planning) to schedule pumping, blending, and demand allocation while limiting seawater intrusion (SWI) and nitrate/salinity exceedances
Key outputs: Dynamic control policies, water-quality and demand forecasts, groundwater-potential and reservoir-level maps, DSS dashboards
Stakeholders: Basin agencies, local utilities, farmer groups, municipalities
Site type: Experimental & decision-support pilot
System focus: Aquifer stress reduction and safe yield under irregular precipitation and growing urban–agri demands
What we test: AI optimization for well operations and allocation, ML prediction of water quality and SWI risk, integration with hydrologic models and multi-source datasets
Key outputs: Risk-aware pumping rules, pollution-hotspot early warnings, demand/quality nowcasts, policy briefs for drought operations
Stakeholders: Water directorates, agricultural offices, local communities
Site type: Water-smart farming system site
System focus: Reliable seasonal supply for agriculture via conjunctive use (surface + groundwater) and quality-constrained blending
What we test: RL-based irrigation scheduling under climate scenarios, sub-district allocation rules, salinity-aware routing, and reservoir level forecasting
Key outputs: Farm-to-district scheduling recommendations, salinity/quality compliance plans, DSS for drought rotations
Stakeholders: Irrigation consortia, regional water authorities, farmer associations
Site type: Water-smart management pilot
System focus: Meeting domestic and irrigation demands while mitigating SWI and pollution in coastal reaches
What we test: GA/RL optimization of pumping/blending and canal operations; ML forecasts for drinking/irrigation water quality, salinity fronts, and demand peaks
Key outputs: Operable control policies, risk maps for SWI and pollution, reservoir and demand forecasts, actionable DSS views
Stakeholders: Water utilities, irrigation districts, local councils, farmer cooperatives