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<p>Water scarcity, exacerbated by population growth and climate change, demands innovative solutions for sustainable water management. This comprehensive review explores artificial intelligence’s (AI) transformative potential in enhancing drainage water reuse systems, addressing critical gaps in traditional methods. Key findings reveal: (1) AI-driven approaches (e.g., machine learning, deep learning) improve water treatment accuracy by 15–25% and reduce energy consumption by 7–30%; (2) operational costs decline by 10–20%, with agricultural reuse efficiencies reaching 29–93%; (3) case studies demonstrate AI’s success in mitigating urban floods (50–70% reduction) and pollutant discharge (15–28% reduction); (4) barriers include data limitations (affecting 60–75% of models), high implementation costs (20–40% above conventional systems), and public skepticism (30–50% resistance). The review highlights AI’s pivotal role in advancing sustainable water reuse, which has the potential to benefit many people in water-scarce regions. Future directions emphasize the use of standardized data protocols, cost-effective solutions, and stakeholder engagement to overcome adoption challenges.</p>

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Artificial Intelligence in Sustainable Drainage Water Reuse: A Comprehensive Review

  • 期刊论文

  • 发表日期：

    2025年9月22日

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  • Layth Saeed Abdulameer,
  • Mushtaq K. Abdalrahem,
  • Najah M. L. Al Maimuri,
  • Ahmed N. Al-Dujaili,
  • Musa Al-Shammari

摘要

Water scarcity, exacerbated by population growth and climate change, demands innovative solutions for sustainable water management. This comprehensive review explores artificial intelligence’s (AI) transformative potential in enhancing drainage water reuse systems, addressing critical gaps in traditional methods. Key findings reveal: (1) AI-driven approaches (e.g., machine learning, deep learning) improve water treatment accuracy by 15–25% and reduce energy consumption by 7–30%; (2) operational costs decline by 10–20%, with agricultural reuse efficiencies reaching 29–93%; (3) case studies demonstrate AI’s success in mitigating urban floods (50–70% reduction) and pollutant discharge (15–28% reduction); (4) barriers include data limitations (affecting 60–75% of models), high implementation costs (20–40% above conventional systems), and public skepticism (30–50% resistance). The review highlights AI’s pivotal role in advancing sustainable water reuse, which has the potential to benefit many people in water-scarce regions. Future directions emphasize the use of standardized data protocols, cost-effective solutions, and stakeholder engagement to overcome adoption challenges.