Sustainable water management is a critical challenge in the era of climate change, as increasing global temperatures, altered precipitation patterns, and more frequent extreme weather events are jeopardizing water availability and quality. Artificial Intelligence (AI) has surfaced as a powerful tool, providing advanced solutions for hydrological modeling, water resource optimization, and disaster preparedness. Decision makers support the use of new technologies such as digital twins, machine learning (ML), remote sensing analytics and decision support systems in order to enhance predictive accuracy and enable adaptive management strategies. However, the use of these technologies in climate resilient water systems faces critical issues like data scarcity, data quality, interpretability challenges, constraints in computational power, and regulatory barriers. In this paper we aim to deliver an in-depth, state of the art analysis of AI applications in water management, their effectiveness, limitations, and future opportunities. We also highlight the importance of interdisciplinary collaboration, more effective integration of data, and ethical AI framework design for sustainable and scalable solutions. Provided that these challenges are addressed, AI has a critical role to play in improving water security and climate resilience for future generations.
Ehmimed, N, Chkouri, MY & Touhafi, A 2026, AI for Climate Change Resilience in Water Management. in N Idrissi, A Hair, Y Saadi, H Chakib, M Erritali, S El Kafhali & M Lazaar (eds), Artificial Intelligence and Green Computing - Proceedings of the 2nd International Conference on Artificial Intelligence and Green Computing ICAIGC 2025. Lecture Notes in Networks and Systems, vol. 1589 LNNS, Springer Nature, pp. 279-292. https://doi.org/10.1007/978-3-032-02312-4_22
Ehmimed, N., Chkouri, M. Y., & Touhafi, A. (2026). AI for Climate Change Resilience in Water Management. In N. Idrissi, A. Hair, Y. Saadi, H. Chakib, M. Erritali, S. El Kafhali, & M. Lazaar (Eds.), Artificial Intelligence and Green Computing - Proceedings of the 2nd International Conference on Artificial Intelligence and Green Computing ICAIGC 2025 (pp. 279-292). (Lecture Notes in Networks and Systems; Vol. 1589 LNNS). Springer Nature. https://doi.org/10.1007/978-3-032-02312-4_22
@inproceedings{e7b5da93cd5f4b10b11c649b38bc5df0,
title = "AI for Climate Change Resilience in Water Management",
abstract = "Sustainable water management is a critical challenge in the era of climate change, as increasing global temperatures, altered precipitation patterns, and more frequent extreme weather events are jeopardizing water availability and quality. Artificial Intelligence (AI) has surfaced as a powerful tool, providing advanced solutions for hydrological modeling, water resource optimization, and disaster preparedness. Decision makers support the use of new technologies such as digital twins, machine learning (ML), remote sensing analytics and decision support systems in order to enhance predictive accuracy and enable adaptive management strategies. However, the use of these technologies in climate resilient water systems faces critical issues like data scarcity, data quality, interpretability challenges, constraints in computational power, and regulatory barriers. In this paper we aim to deliver an in-depth, state of the art analysis of AI applications in water management, their effectiveness, limitations, and future opportunities. We also highlight the importance of interdisciplinary collaboration, more effective integration of data, and ethical AI framework design for sustainable and scalable solutions. Provided that these challenges are addressed, AI has a critical role to play in improving water security and climate resilience for future generations.",
author = "Nadir Ehmimed and Chkouri, \{Mohamed Yassin\} and Abdellah Touhafi",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.",
year = "2026",
doi = "10.1007/978-3-032-02312-4\_22",
language = "English",
isbn = "978-3-032-02311-7",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Nature",
pages = "279--292",
editor = "Najlae Idrissi and Abdellatif Hair and Youssef Saadi and Houda Chakib and Mohammed Erritali and \{El Kafhali\}, Said and Mohamed Lazaar",
booktitle = "Artificial Intelligence and Green Computing - Proceedings of the 2nd International Conference on Artificial Intelligence and Green Computing ICAIGC 2025",
}