AI-Driven Territorial Intelligence for Sustainable Water Management

This study explores the role of Artificial Intelligence (Al)-driven territorial intelligence in enhancing sustainable water resource management. Faced with increasing challenges such as climate change, population growth, and unsustainable water use, traditional water governance approaches often fail to account for spatial, socio-economic, and hydrological complexities. The concept of territorial intelligence is introduced as a framework for integrating spatial data, institutional dynamics, and AI-based analytical tools to support informed decision-making. The paper proposes a conceptual and operational framework combining data infrastructures, machine learning techniques, and territorial governance mechanisms. It highlights the importance of integrating multi-source data, including climatic, hydrological, socioeconomic, and geospatial datasets, to generate predictive insights and scenario simulations. AI-driven systems enable the identification of patterns, forecasting of water demand, and optimization of resource allocation across different territorial scales. Applications across multiple sectors, including surface water, groundwater, agriculture, urban systems, and ecosystems, demonstrate the potential of territorial intelligence to improve governance efficiency, sustainability, and resilience. However, the study also identifies key challenges related to data quality, interoperability, ethical risks, and governance structures. Ultimately, AI-driven territorial intelligence is presented as a transformative approach that supports integrated water resource management, enhances policy effectiveness, and promotes sustainable and equitable water governance.