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This study presents a comprehensive bibliometric analysis and critical review of artificial intelligence (AI) applications in lake water quality management, examining 623 peer-reviewed publications from the Scopus database between 2016 and 2025. The research employed a systematic methodology integrating tools, e.g., Biblioshiny and Scimago Graphica, to analyze publication trends, collaborative networks, and thematic evolution within this rapidly expanding field. Results reveal a remarkable annual growth rate of 30.26% in publications, with 2268 authors contributing across 1699 distinct keywords, demonstrating significant international collaboration and research momentum. China and the United States emerged as leading contributors, with extensive collaborative networks facilitating knowledge exchange and methodological standardization. The bibliometric analysis identified four primary AI methodological categories: shallow learning methods (e.g., random forests and support vector machines), deep learning approaches (e.g., Convolutional neural networks and Long Short-Term Memory networks), optimization algorithms (e.g., genetic algorithms and particle swarm optimization), and hybrid models combining multiple algorithmic strategies. Critical evaluation of real-world implementations revealed that hybrid models achieve superior performance in spatiotemporal water quality prediction, while transformer-based architectures demonstrate exceptional performance. The study establishes sustainability implications across six sustainable development goals (SDGs), highlighting how AI-driven monitoring systems enhance early threat detection, optimize ecosystem service quantification, and support evidence-based policymaking. Future research directions emphasize the need for explainable AI frameworks, federated learning architectures, and community-engaged monitoring systems that bridge technological innovation with environmental justice principles, positioning AI as a catalyst for sustainable lake management practices in an era of rapid environmental change.

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Leveraging AI for Sustainable Water Quality Management in Lakes: Bibliometric Insights and Critical Review

  • 章节

  • 发表日期：

    2026年

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  • Ahmed M. Saqr,
  • Moustafa Gamal Snousy,
  • Joseph Omeiza Alao,
  • Rakesh Ranjan Thakur,
  • Debabrata Nandi,
  • Abdennabi Alitane,
  • Mohamed Ouarani,
  • Meriame Mohajane,
  • Ramesh Raj Pant,
  • Jianhua Wu,
  • Mohamed Abdellatief

摘要

This study presents a comprehensive bibliometric analysis and critical review of artificial intelligence (AI) applications in lake water quality management, examining 623 peer-reviewed publications from the Scopus database between 2016 and 2025. The research employed a systematic methodology integrating tools, e.g., Biblioshiny and Scimago Graphica, to analyze publication trends, collaborative networks, and thematic evolution within this rapidly expanding field. Results reveal a remarkable annual growth rate of 30.26% in publications, with 2268 authors contributing across 1699 distinct keywords, demonstrating significant international collaboration and research momentum. China and the United States emerged as leading contributors, with extensive collaborative networks facilitating knowledge exchange and methodological standardization. The bibliometric analysis identified four primary AI methodological categories: shallow learning methods (e.g., random forests and support vector machines), deep learning approaches (e.g., Convolutional neural networks and Long Short-Term Memory networks), optimization algorithms (e.g., genetic algorithms and particle swarm optimization), and hybrid models combining multiple algorithmic strategies. Critical evaluation of real-world implementations revealed that hybrid models achieve superior performance in spatiotemporal water quality prediction, while transformer-based architectures demonstrate exceptional performance. The study establishes sustainability implications across six sustainable development goals (SDGs), highlighting how AI-driven monitoring systems enhance early threat detection, optimize ecosystem service quantification, and support evidence-based policymaking. Future research directions emphasize the need for explainable AI frameworks, federated learning architectures, and community-engaged monitoring systems that bridge technological innovation with environmental justice principles, positioning AI as a catalyst for sustainable lake management practices in an era of rapid environmental change.