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Predictive Modeling and Generative AI for Anticipating Water Shortages
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Author(s): Oussama El Azzouzy (Science Faculty of Ain-Chock, Hassan II University, Morocco), Tarik Chanyour (Science Faculty of Ain-Chock, Hassan II University, Morocco), Said Jai Andaloussi (Science Faculty of Ain-Chock, Hassan II University, Morocco), Khadija El Fellah (Laboratory of Research in Informatics, FS, UIT, Kenitra, Morocco), Ikram El Azami (Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco)and Adil El Makrani (Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco)
Copyright: 2026
Pages: 44
Source title:
Computational Intelligence and Optimization Methods for Sustainable Water Management
Source Author(s)/Editor(s): Yassine Ezaier (Hassan II University, Casablanca, Morocco), Rajae Gaamouche (Moroccan School of Engineering Sciences, Rabat, Morocco)and Mohamed Lahby (Hassan II University, Casablanca, Morocco)
DOI: 10.4018/979-8-3373-2700-6.ch006
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Abstract
Faced with the intensification of water shortages caused by climate change and human pressures, this chapter presents a hybrid architecture combining predictive modelling and extreme scenario generation to strengthen territorial resilience. The approach is based on the integration of time series transformers and deep generative models to anticipate the evolution of hydrological variables and simulate critical events. In the absence of sufficiently rich real data, a set of realistic synthetic data was constructed from hydrological distributions, seasonal cycles and stochastic processes, then qualitatively validated. This game allows a rigorous evaluation of predictive and generative performance. Results demonstrate the complementarity of the two dimensions to improve risk visualization and activation of early responses. The chapter also discusses technical limitations, ethical issues, and proposes concrete perspectives for integration into digital twins, warning systems or decision support tools for climate adaptation.
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