Artificial Intelligence in Climate-Resilient Water Management: A Systematic Review of Applications, Challenges, and Future Directions

被引：0
作者：
Layth Abdulameer [1 ]
Mahmoud Saleh Al-Khafaji [2 ]
Aysar Tuma Al-Awadi [1 ]
Najah M. L. Al Maimuri [3 ]
Musa Al-Shammari [1 ]
Ahmed N. Al-Dujaili [4 ]
undefined DhiyaAl‑Jumeily [5 ]
机构：
[1] Department of Civil Engineering, College of Engineering, University of Karbala, Karbala
[2] Department of Water Resources Engineering, College of Engineering, University of Baghdad, Baghdad
[3] Building and Construction Technologies Engineering Department, College of Engineering and Engineering Technologies, Al-Mustaqbal University, Hillah, Babylon
[4] Petroleum Engineering Department, Amirkabir University of Technology, No. 350, Hafez Ave, Valiasr Square, Tehran
[5] Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool
来源：
Water Conservation Science and Engineering | 2025年 / 10卷 / 1期
关键词：
Artificial intelligence; Climate change; Hydrological forecasting; Machine learning; Sustainable development; Water resource management;
D O I：
10.1007/s41101-025-00371-2
中图分类号：
学科分类号：
摘要：
Climate change intensifies global water insecurity through escalating hydrological extremes, deteriorating water quality, and aging infrastructure, necessitating transformative solutions. This systematic review evaluates the role of artificial intelligence (AI) in advancing climate-resilient water management. Key findings reveal that AI models—particularly long short-term memory (LSTM) and hybrid physics-informed neural networks—achieve superior accuracy in hydrological forecasting (Nash–Sutcliffe efficiency > 0.90), enabling reliable predictions of water availability, droughts, and floods. AI-driven optimization enhances water distribution efficiency by 15–30% in case studies, while IoT-integrated systems reduce agricultural water waste by 20–40%. However, critical challenges persist: (1) data inequity, with 70% of AI applications concentrated in temperate, data-rich regions, neglecting arid and low-income areas; (2) model interpretability gaps, as “black-box” algorithms hinder stakeholder trust; and (3) policy-technical misalignment, where siloed governance stifles scalable AI adoption. The review underscores the urgency of hybrid AI-physics frameworks to balance accuracy with explainability, decentralized data ecosystems to empower marginalized communities, and ethical governance protocols to address algorithmic bias and equity. Future research should prioritize integrating multi-source data to enhance model transparency, to ensure sustainable and inclusive water management strategies. By bridging technological innovation with systemic resilience, AI emerges as a critical tool in mitigating climate impacts and securing global water security. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
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