{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T16:49:25Z","timestamp":1773074965738,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T00:00:00Z","timestamp":1770940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"RCM2+, Lus\u00f3fona University\/COFAC"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Environments"],"abstract":"<jats:p>Water allocation remains a critical global challenge due to increasing scarcity, competing sectoral demands, and environmental pressures, requiring approaches that balance efficiency, equity, and ecosystem sustainability while facing the inherent contextual uncertainty. Recent developments in operations research and statistical learning have paved the way for a new paradigm in nonlinear modeling under uncertainty, i.e., contextual optimization. This emerging framework seamlessly combines predictive analytics with robust optimization techniques to address sustainable decision-making problems in dynamic environments. In this study, we introduce a novel learning-enabled optimization method that extends the current domain of contextual stochastic optimization. Leveraging regression-based statistical learning techniques, our approach enhances predictive accuracy and reinforces decision robustness. Unlike traditional methods, which often struggle with parameter variability and unbounded solution spaces, our model establishes clear predictive bounds that reduce the uncertainty region, thereby minimizing deviations from optimality. We apply our methodology to water allocation in Tunisia\u2019s coastal tourism sector (2010\u20132022), where resource availability is constrained and highly variable. While developed for this specific context, the framework is transferable to similar Mediterranean arid\/semi-arid tourism regions subject to certain data and governance conditions. The proposed approach accurately predicts water demand and optimizes the allocation of diverse water sources, contributing to sustainable water resource management. This paper presents both theoretical foundations and practical applications of our method in complex, data-driven decision environments, demonstrating its relevance for achieving sustainable development goals.<\/jats:p>","DOI":"10.3390\/environments13020105","type":"journal-article","created":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:09:32Z","timestamp":1770998972000},"page":"105","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Water Resource Allocation: A Learning-Based Optimization Framework for Sustainable Decision-Making Under Uncertainty"],"prefix":"10.3390","volume":"13","author":[{"given":"Marwa","family":"Mallek","sequence":"first","affiliation":[{"name":"Olid Laboratory, Faculty of Economics and Management of Sfax, University of Sfax, Sfax 3018, Tunisia"}]},{"given":"Boukthir","family":"Haddar","sequence":"additional","affiliation":[{"name":"Data Engineering and Semantics Research Unit, Faculty of Sciences of Sfax, University of Sfax, Sfax 3018, Tunisia"}]},{"given":"Mohamed Ali","family":"Elleuch","sequence":"additional","affiliation":[{"name":"Olid Laboratory, Faculty of Economics and Management of Sfax, University of Sfax, Sfax 3018, Tunisia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2016-3603","authenticated-orcid":false,"given":"Francisco Silva","family":"Pinto","sequence":"additional","affiliation":[{"name":"RCM2+, Lus\u00f3fona University, Campo Grande 376, 1749-024 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2961-1839","authenticated-orcid":false,"given":"Tiago","family":"Cetrulo","sequence":"additional","affiliation":[{"name":"RCM2+, Lus\u00f3fona University, Campo Grande 376, 1749-024 Lisboa, Portugal"},{"name":"School of Applied Sciences, University of Campinas (UNICAMP), Campinas 13083-970, Brazil"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Liu, X., Qi, H., Jia, S., Guo, Y., and Liu, Y. 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