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<p>Timely and effective water resource management that promotes resilience is fundamental to numerous sustainable development goals. This study explores the application of Artificial Intelligence (AI) and Machine Learning (ML) methodologies to address critical challenges in water governance, such as climate change, urbanization, and agricultural intensification. Key findings demonstrate that AI-driven tools, including real-time water quality monitoring and predictive modeling, significantly enhance decision-making for sustainable water allocation and flood prediction. Methodologically, we integrate interdisciplinary approaches—combining engineering, social sciences, and remote sensing—to develop scalable solutions for the Lake Victoria Basin as a case study. Our research highlights the synergy between AI/ML and socio-technical systems, revealing a 99% accuracy in water demand forecasting models and improved resilience in disaster management frameworks. These results underscore the transformative potential of AI/ML in harmonizing ecological, economic, and policy dimensions for future water governance.</p>

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Building resilience in water resource management using artificial intelligence and interdisciplinary strategies for sustainable water security and future governance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月20日

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  • Venkata Narasareddy Annapareddy,
  • Manoj Kollam,
  • Kishore Challa,
  • Ravi Kumar Vankayalapati,
  • Murali Malempati,
  • Zakera Yasmeen

摘要

Timely and effective water resource management that promotes resilience is fundamental to numerous sustainable development goals. This study explores the application of Artificial Intelligence (AI) and Machine Learning (ML) methodologies to address critical challenges in water governance, such as climate change, urbanization, and agricultural intensification. Key findings demonstrate that AI-driven tools, including real-time water quality monitoring and predictive modeling, significantly enhance decision-making for sustainable water allocation and flood prediction. Methodologically, we integrate interdisciplinary approaches—combining engineering, social sciences, and remote sensing—to develop scalable solutions for the Lake Victoria Basin as a case study. Our research highlights the synergy between AI/ML and socio-technical systems, revealing a 99% accuracy in water demand forecasting models and improved resilience in disaster management frameworks. These results underscore the transformative potential of AI/ML in harmonizing ecological, economic, and policy dimensions for future water governance.