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Project Topic
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This project focuses on utilizing Artificial intelligence (AI) optimization and prediction techniques for water management in (1) the Ras Jebel coastal area (RJC) in Tunisia; (2) the Coastal Constantinois and Seybouse basins (CCB&SB) in North-East of Algeria; (3) in the Capitanata coastal irrigation district (CID) in Apulia region in southern Italy; and (4) in the coastal Nile Delta Basin (NDB) in Egypt. All these areas are characterized by a typical Mediterranean climate and may face similar issues of water scarcity, pollution, soil salinization, and salt water intrusion (SWI) in the groundwater. The objective is to characterize water resources, demands, and requests to meet irrigation and domestic needs under critical situations. The core aim is to optimize water usage by employing AI methods to identify management strategies under current and projected climatic conditions. Advanced machine learning (ML) techniques and hydrologic models will predict salinization levels, water pollution levels, groundwater level, water demands, and optimizing freshwater resource usage. The project will employ optimization techniques like Genetic Algorithms, Reinforcement Learning, and AI planning algorithms tailored to formulate control policies for water resource management in the RJC, CCB&SB, CID, and NDB, as case studies.
Given water scarcity and irregular precipitation patterns, addressing these challenges requires the latest technologies for planning, managing, and optimizing water usage. Stakeholder involvement is crucial for the effective application of proposed techniques, leveraging their knowledge and insights. The project also emphasizes employing advanced water accounting techniques and purchasing sensors for data collection, facilitating informed decision-making and sustainable water resource use. ML-driven predictive analytics will offer optimized solutions, while involving local stakeholders ensures alignment with regional needs.
Until now, water management has followed strict regulations, corresponding to a top-down approach. In this project, we will use a bottom-up approach, involving stakeholders and government authorities in the design phase by soliciting their needs, such as water release for irrigation and domestic uses. They will be actively involved during identification and validation of the scenarios to be applied, through the project inclusive approach. The AI4Water project results will lead to more flexible regulations, enabling operators to comprehend trade-offs among different interests in water resource management, reducing the problem of over-prioritizing one objective to the detriment of others.
The AI optimization techniques developed represent a first step towards a more complex planning formalization, considering various factors such as temporal and causal relations among the actions of different agents, including multi-source operators. Effective planning in smart water management decisions is crucial to pre-empt costly remedial actions in critical situations. Comprehensive plans allow stakeholders to anticipate and mitigate potential challenges, minimizing the effects of extreme adverse events related to water scarcity or contamination. Proactive measures ensure efficient allocation and utilization of water resources, averting the need for expensive emergency interventions. Prioritizing planning in water management strategies ensures resilience and promotes long-term sustainability and resilience in the face of evolving environmental conditions.
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