IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i20p13231-d942635.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/20/13231/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/20/13231/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rabia Koklu & Bulent Sengorur & Bayram Topal, 2010. "Water Quality Assessment Using Multivariate Statistical Methods—A Case Study: Melen River System (Turkey)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(5), pages 959-978, March.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sayiter Yıldız & Can Bülent Karakuş, 2020. "Estimation of irrigation water quality index with development of an optimum model: a case study," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(5), pages 4771-4786, June.
    2. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    3. Kichul Jung & Deg-Hyo Bae & Myoung-Jin Um & Siyeon Kim & Seol Jeon & Daeryong Park, 2020. "Evaluation of Nitrate Load Estimations Using Neural Networks and Canonical Correlation Analysis with K-Fold Cross-Validation," Sustainability, MDPI, vol. 12(1), pages 1-17, January.
    4. Fabiana Liar Agudo & Bárbara Stolte Bezerra & José Alcides Gobbo Júnior, 2024. "An overview of Brazilian companies' readiness to implement industrial symbiosis," Business Strategy and the Environment, Wiley Blackwell, vol. 33(2), pages 1066-1080, February.
    5. Ana Gomes & José Pires & Sónia Figueiredo & Rui Boaventura, 2014. "Optimization of River Water Quality Surveys by Multivariate Analysis of Physicochemical, Bacteriological and Ecotoxicological Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1345-1361, March.
    6. Ranković, Vesna & Radulović, Jasna & Radojević, Ivana & Ostojić, Aleksandar & Čomić, Ljiljana, 2010. "Neural network modeling of dissolved oxygen in the Gruža reservoir, Serbia," Ecological Modelling, Elsevier, vol. 221(8), pages 1239-1244.
    7. Junguo, Hu & Guomo, Zhou & Xiaojun, Xu, 2013. "Using an improved back propagation neural network to study spatial distribution of sunshine illumination from sensor network data," Ecological Modelling, Elsevier, vol. 266(C), pages 86-96.
    8. Shanshan Wang & Joe Wiart, 2020. "Sensor-Aided EMF Exposure Assessments in an Urban Environment Using Artificial Neural Networks," IJERPH, MDPI, vol. 17(9), pages 1-15, April.
    9. West, David & Dellana, Scott, 2011. "An empirical analysis of neural network memory structures for basin water quality forecasting," International Journal of Forecasting, Elsevier, vol. 27(3), pages 777-803, July.
    10. Jian Sha & Zeli Li & Dennis P. Swaney & Bongghi Hong & Wei Wang & Yuqiu Wang, 2014. "Application of a Bayesian Watershed Model Linking Multivariate Statistical Analysis to Support Watershed-Scale Nitrogen Management in China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(11), pages 3681-3695, September.
    11. Ahmad Khazaee Poul & Mojtaba Shourian & Hadi Ebrahimi, 2019. "A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2907-2923, June.
    12. Tanujit Chakraborty & Ashis Kumar Chakraborty & Zubia Mansoor, 2019. "A hybrid regression model for water quality prediction," OPSEARCH, Springer;Operational Research Society of India, vol. 56(4), pages 1167-1178, December.
    13. Tat Pham Van & Pham Nu Ngoc Han & Minh Phap Dao, 2017. "Modelling of Dissolved Oxygen in Thi Vai River Water Incorporating Artificial Neural Network and Multivariable Regression," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 7(1), pages 11-18, November.
    14. Luke Mosley & Benjamin Zammit & Emily Leyden & Theresa Heneker & Matthew Hipsey & Dominic Skinner & Kane Aldridge, 2012. "The Impact of Extreme Low Flows on the Water Quality of the Lower Murray River and Lakes (South Australia)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(13), pages 3923-3946, October.
    15. Ayman Awadallah & Mohsen Yousry, 2012. "Identifying Homogeneous Water Quality Regions in the Nile River Using Multivariate Statistical Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(7), pages 2039-2055, May.
    16. Zhang, Zixiong & Gong, Yicheng & Wang, Zhongjing, 2018. "Accessible remote sensing data based reference evapotranspiration estimation modelling," Agricultural Water Management, Elsevier, vol. 210(C), pages 59-69.
    17. Pavitra Kumar & Sai Hin Lai & Jee Khai Wong & Nuruol Syuhadaa Mohd & Md Rowshon Kamal & Haitham Abdulmohsin Afan & Ali Najah Ahmed & Mohsen Sherif & Ahmed Sefelnasr & Ahmed El-Shafie, 2020. "Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models," Sustainability, MDPI, vol. 12(11), pages 1-26, May.
    18. Vlontzos, G. & Pardalos, P.M., 2017. "Assess and prognosticate green house gas emissions from agricultural production of EU countries, by implementing, DEA Window analysis and artificial neural networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 155-162.
    19. Xiaoliang Meng & Chao Xu & Xinxia Liu & Junming Bai & Wenhan Zheng & Hao Chang & Zhuo Chen, 2018. "An Ontology-Underpinned Emergency Response System for Water Pollution Accidents," Sustainability, MDPI, vol. 10(2), pages 1-18, February.
    20. Mehmet Kayakuş, 2020. "The Estimation of Turkey's Energy Demand Through Artificial Neural Networks and Support Vector Regression Methods," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 8(2), pages 227-236, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13231-:d:942635. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.