Data

The data used in this project comes from multiple sources to ensure comprehensive coverage of relevant parameters across selected Mediterranean regions, including Italy, Algeria, Tunisia, and Egypt. Historical and current datasets are collected from government agencies, research institutions, meteorological stations, and hydrological databases. In-situ measurements such as water levels, salinity, and flow rates provide localized insights, while field surveys add site-specific information. Remote sensing data and satellite imagery are employed to capture large-scale environmental variables, including soil salinity, vegetation health, and surface water conditions. The datasets include precipitation, temperature, evaporation, groundwater level, land use, and demography. Data cleansing and cross-validation are applied to ensure consistency and accuracy. Water quality is assessed using indices like DWQI and IWQI, along with parameters such as pH, heavy metals, nitrates, and sodium adsorption ratio. Salinity mapping combines satellite and ground data to identify affected areas. Machine learning models, including time-series forecasting, are used to predict saltwater intrusion, groundwater salinity, and water quality.

Equity Considerations

AI4Water places strong emphasis on equitable access to water resources by incorporating transparency and stakeholder engagement into every stage of the project. Through the use of open-source tools and the public release of data and AI systems under the GNU GPLv3 License, the project ensures that all actors, including researchers, policymakers, and local communities, have equal opportunities to benefit from and contribute to its outcomes. The selection of diverse case studies in Algeria, Egypt, Tunisia, and Italy highlights the commitment to addressing water scarcity challenges across varied socioeconomic and climatic contexts.

The project promotes equity in decision-making by integrating local data, engaging stakeholders in collaborative planning, and simulating scenarios tailored to community-specific water needs. Activities such as the optimization of water allocation and scenario simulations aim to improve fairness among competing users, especially between agricultural, industrial, and domestic sectors. Key Performance Indicators such as KPI2 specifically target improved transparency and equity in water distribution, ensuring no group is disproportionately disadvantaged. Living labs in each test area allow for local feedback to refine management strategies that respect community priorities. This inclusive approach is essential to achieving sustainable and just water governance under current and future climate pressures.

Ethical Considerations

Ethically, AI4Water is guided by a commitment to transparency, data responsibility, and respect for local contexts. By aligning with the FAIR Data Principles and releasing sample datasets only with proper authorization and without breaching national data restrictions, the project ensures that data use respects privacy and sovereignty. The decision to employ GNU GPLv3 licensing guarantees that AI tools remain freely accessible and modifiable, promoting ethical innovation and discouraging proprietary exploitation of critical water management technologies. Predictive models are developed based on diverse and validated datasets to avoid biased or misleading outputs, which could otherwise lead to inequitable or harmful water policies. Ethical engagement is reinforced through participatory planning with stakeholders, ensuring the legitimacy and social acceptance of proposed solutions. Additionally, AI4Water addresses ethical sustainability by supporting SDGs related to environmental health, social well-being, and intergenerational equity, especially in the face of climate-induced stressors. The project also emphasizes the responsible use of machine learning to complement, not replace, traditional knowledge systems, ensuring technology supports rather than dominates local water governance.