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Recent Advances in Surface Water Quality Prediction Using Artificial Intelligence Models

Author

Listed:
  • Qingqing Zhang

    (Tianjin University)

  • Xue-yi You

    (Tianjin University)

Abstract

Accurate water quality prediction plays a vital role in sustainable water management. The artificial intelligence models commonly used in water management including artificial neural networks, long short-term memory, support vector machine, adaptive neuro-fuzzy inference are reviewed. The hybrid models, artificial intelligence coupled with decomposition and optimization algorithms, are thoroughly discussed. After a brief introduction of each model, a review of recent publications to understand the potential and application of these models in surface water quality modeling is proposed. Based on the selected 60 papers published over the past decade (2013–2023), the statistical results in terms of data pre-processing, input and output parameters, modeling approaches, and performance evaluation are analyzed to reveal the latest trends. Artificial neural network has flexible variants to suit many scenarios and long short-term memory model has the advantage of processing time series data. Most remarkable performance in the single and hybrid algorithms is found. Hybrid algorithms generate more satisfactory results in predicting accuracy. The application of these models improves the decision-making mechanisms for environmental governance and show immense potential for various applications.

Suggested Citation

  • Qingqing Zhang & Xue-yi You, 2024. "Recent Advances in Surface Water Quality Prediction Using Artificial Intelligence Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 235-250, January.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:1:d:10.1007_s11269-023-03666-y
    DOI: 10.1007/s11269-023-03666-y
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    References listed on IDEAS

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    1. Jingjing Xia & Jin Zeng, 2022. "Environmental Factors Assisted the Evaluation of Entropy Water Quality Indices with Efficient Machine Learning Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2045-2060, April.
    2. Zaher Mundher Yaseen & Majeed Mattar Ramal & Lamine Diop & Othman Jaafar & Vahdettin Demir & Ozgur Kisi, 2018. "Hybrid Adaptive Neuro-Fuzzy Models for Water Quality Index Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(7), pages 2227-2245, May.
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    Cited by:

    1. Wenwen Wu & Zilin Wei & Lifeng Wu, 2024. "Public Satisfaction with Water Quality Under the Implementation of Water Quality Monitor Standard System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(11), pages 4197-4212, September.

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