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An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters

Author

Listed:
  • Binglin Li

    (School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China)

  • Hao Xu

    (School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China)

  • Yufeng Lian

    (School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China)

  • Pai Li

    (School of Computer and Automation, Wuhan Technology and Business University, Wuhan 430070, China)

  • Yong Shao

    (School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China)

  • Chunyu Tan

    (School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China)

Abstract

Prediction of water quality parameters is a significant aspect of contemporary green development and ecological restoration. However, the conventional water quality prediction models have limited accuracy and poor generalization capability. This study aims to develop a dependable prediction model for ammonia nitrogen concentration in water quality parameters. Based on the characteristics of the long-term dependence of water quality parameters, the unique memory ability of the Long Short-Term Memory (LSTM) neural network was utilized to predict water quality parameters. To improve the accuracy of the LSTM prediction model, the ammonia nitrogen data were decomposed using Empirical Modal Decomposition (EMD), and then the parameters of the LSTM model were optimized using the Improved Whale Optimization Algorithm (IWOA), and a combined prediction model based on EMD-IWOA-LSTM was proposed. The study outcomes demonstrate that EMD-IWOA-LSTM displays improved prediction accuracy with reduced RootMean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) in comparison to the LSTM and IWOA-LSTM approaches. These research findings better enable the monitoring and prediction of water quality parameters, offering a novel approach to preventing water pollution rather than merely treating it afterwards.

Suggested Citation

  • Binglin Li & Hao Xu & Yufeng Lian & Pai Li & Yong Shao & Chunyu Tan, 2023. "An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters," Sustainability, MDPI, vol. 15(24), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16816-:d:1299635
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    References listed on IDEAS

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    1. Vinay Kumar Gautam & Chaitanya B. Pande & Kanak N. Moharir & Abhay M. Varade & Nitin Liladhar Rane & Johnbosco C. Egbueri & Fahad Alshehri, 2023. "Prediction of Sodium Hazard of Irrigation Purpose using Artificial Neural Network Modelling," Sustainability, MDPI, vol. 15(9), pages 1-17, May.
    2. Jinping Zhang & Honglin Xiao & Hongyuan Fang, 2022. "Component-based Reconstruction Prediction of Runoff at Multi-time Scales in the Source Area of the Yellow River Based on the ARMA Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 433-448, January.
    3. Xinchang Liu & Bolong Liu, 2023. "A Hybrid Time Series Model for Predicting the Displacement of High Slope in the Loess Plateau Region," Sustainability, MDPI, vol. 15(6), pages 1-26, March.
    4. Yehua Li & Marc G. Genton, 2009. "Single‐Index Additive Vector Autoregressive Time Series Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 369-388, September.
    5. Yunyi Li & Minhua Shao & Lijun Sun & Xinmiao Wang & Shizhao Song, 2023. "Research on Demand Price Elasticity Based on Expressway ETC Data: A Case Study of Shanghai, China," Sustainability, MDPI, vol. 15(5), pages 1-11, March.
    6. Yi Wang & Yuhan Cheng & He Liu & Qing Guo & Chuanjun Dai & Min Zhao & Dezhao Liu, 2023. "A Review on Applications of Artificial Intelligence in Wastewater Treatment," Sustainability, MDPI, vol. 15(18), pages 1-28, September.
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