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Water Quality Prediction Based on LSTM and Attention Mechanism: A Case Study of the Burnett River, Australia

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  • Honglei Chen

    (Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha 410004, China
    School of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China)

  • Junbo Yang

    (Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha 410004, China
    School of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China)

  • Xiaohua Fu

    (Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha 410004, China
    School of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China)

  • Qingxing Zheng

    (Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha 410004, China
    School of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China)

  • Xinyu Song

    (Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha 410004, China
    School of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China)

  • Zeding Fu

    (School of Hydraulic and Environmental Engineering, Changsha University of Science & Technology, Changsha 410114, China)

  • Jiacheng Wang

    (Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha 410004, China
    School of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China)

  • Yingqi Liang

    (Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha 410004, China
    School of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China)

  • Hailong Yin

    (Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha 410004, China
    School of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China)

  • Zhiming Liu

    (Department of Biology, Eastern New Mexico University, Portales, NM 88130, USA)

  • Jie Jiang

    (Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha 410004, China
    School of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China)

  • He Wang

    (Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha 410004, China
    School of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China)

  • Xinxin Yang

    (Ecological Environment Management and Assessment Center, Central South University of Forestry and Technology, Changsha 410004, China
    School of Environmental Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China)

Abstract

Prediction of water quality is a critical aspect of water pollution control and prevention. The trend of water quality can be predicted using historical data collected from water quality monitoring and management of water environment. The present study aims to develop a long short-term memory (LSTM) network and its attention-based (AT-LSTM) model to achieve the prediction of water quality in the Burnett River of Australia. The models developed in this study introduced an attention mechanism after feature extraction of water quality data in the section of Burnett River considering the effect of the sequences on the prediction results at different moments to enhance the influence of key features on the prediction results. This study provides one-step-ahead forecasting and multistep forward forecasting of dissolved oxygen (DO) of the Burnett River utilizing LSTM and AT-LSTM models and the comparison of the results. The research outcomes demonstrated that the inclusion of the attention mechanism improves the prediction performance of the LSTM model. Therefore, the AT-LSTM-based water quality forecasting model, developed in this study, demonstrated its stronger capability than the LSTM model for informing the Water Quality Improvement Plan of Queensland, Australia, to accurately predict water quality in the Burnett River.

Suggested Citation

  • Honglei Chen & Junbo Yang & Xiaohua Fu & Qingxing Zheng & Xinyu Song & Zeding Fu & Jiacheng Wang & Yingqi Liang & Hailong Yin & Zhiming Liu & Jie Jiang & He Wang & Xinxin Yang, 2022. "Water Quality Prediction Based on LSTM and Attention Mechanism: A Case Study of the Burnett River, Australia," Sustainability, MDPI, vol. 14(20), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13231-:d:942635
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    References listed on IDEAS

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    2. Singh, Kunwar P. & Basant, Ankita & Malik, Amrita & Jain, Gunja, 2009. "Artificial neural network modeling of the river water quality—A case study," Ecological Modelling, Elsevier, vol. 220(6), pages 888-895.
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