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Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment

Author

Listed:
  • Ping Liu

    (School of Information Engineering, Yangzhou University, Yangzhou 225127, China)

  • Jin Wang

    (School of Information Engineering, Yangzhou University, Yangzhou 225127, China
    School of Computer & Communication Engineering, Changsha University of Science & Technology, Changsha 410114, China
    School of Information Science and Engineering, Fujian University of Technology, Fujian 350118, China)

  • Arun Kumar Sangaiah

    (School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India)

  • Yang Xie

    (Yangzhou Municipal Bureau of Ecology and Environment, Yangzhou 225007, China)

  • Xinchun Yin

    (Guangling College, Yangzhou University, Yangzhou 225000, China)

Abstract

This research paper focuses on a water quality prediction model which requires high-quality data. In the process of construction and operation of smart water quality monitoring systems based on Internet of Things (IoT), more and more big data are produced at a high speed, which has made water quality data complicated. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, a drinking-water quality model was designed and established to predict water quality big data with the help of the advanced deep learning (DL) theory in this paper. The drinking-water quality data measured by the automatic water quality monitoring station of Guazhou Water Source of the Yangtze River in Yangzhou were utilized to analyze the water quality parameters in detail, and the prediction model was trained and tested with monitoring data from January 2016 to June 2018. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and accurately revealed the future developing trend of water quality, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of drinking water.

Suggested Citation

  • Ping Liu & Jin Wang & Arun Kumar Sangaiah & Yang Xie & Xinchun Yin, 2019. "Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment," Sustainability, MDPI, vol. 11(7), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:2058-:d:220636
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    References listed on IDEAS

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    1. Erfan Babaee Tirkolaee & Ali Asghar Rahmani Hosseinabadi & Mehdi Soltani & Arun Kumar Sangaiah & Jin Wang, 2018. "A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services," Sustainability, MDPI, vol. 10(5), pages 1-21, April.
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    Cited by:

    1. Song, Chenyu & Zhang, Haiping, 2020. "Study on turbidity prediction method of reservoirs based on long short term memory neural network," Ecological Modelling, Elsevier, vol. 432(C).
    2. Mosleh Hmoud Al-Adhaileh & Fawaz Waselallah Alsaade, 2021. "Modelling and Prediction of Water Quality by Using Artificial Intelligence," Sustainability, MDPI, vol. 13(8), pages 1-18, April.
    3. Allison Lassiter & Nicole Leonard, 2022. "A systematic review of municipal smart water for climate adaptation and mitigation," Environment and Planning B, , vol. 49(5), pages 1406-1430, June.
    4. Heelak Choi & Sang-Ik Suh & Su-Hee Kim & Eun Jin Han & Seo Jin Ki, 2021. "Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction," Sustainability, MDPI, vol. 13(19), pages 1-11, September.
    5. Kagiso Samuel More & Christian Wolkersdorfer, 2022. "Predicting and Forecasting Mine Water Parameters Using a Hybrid Intelligent System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2813-2826, June.
    6. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
    7. Eric Hitimana & Gaurav Bajpai & Richard Musabe & Louis Sibomana & Jayavel Kayalvizhi, 2021. "Implementation of IoT Framework with Data Analysis Using Deep Learning Methods for Occupancy Prediction in a Building," Future Internet, MDPI, vol. 13(3), pages 1-19, March.
    8. El Bilali, Ali & Taleb, Abdeslam & Brouziyne, Youssef, 2021. "Groundwater quality forecasting using machine learning algorithms for irrigation purposes," Agricultural Water Management, Elsevier, vol. 245(C).
    9. Wessam El-Ssawy & Hosam Elhegazy & Heba Abd-Elrahman & Mohamed Eid & Niveen Badra, 2023. "Identification of the best model to predict optical properties of water," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6781-6797, July.
    10. Nadine Bachmann & Shailesh Tripathi & Manuel Brunner & Herbert Jodlbauer, 2022. "The Contribution of Data-Driven Technologies in Achieving the Sustainable Development Goals," Sustainability, MDPI, vol. 14(5), pages 1-33, February.
    11. Docheshmeh Gorgij, A. & Askari, Gh & Taghipour, A.A. & Jami, M. & Mirfardi, M., 2023. "Spatiotemporal Forecasting of the Groundwater Quality for Irrigation Purposes, Using Deep Learning Method: Long Short-Term Memory (LSTM)," Agricultural Water Management, Elsevier, vol. 277(C).
    12. Xiaonan Ji & Jianghai Chen & Yali Guo, 2022. "A Multi-Dimensional Investigation on Water Quality of Urban Rivers with Emphasis on Implications for the Optimization of Monitoring Strategy," Sustainability, MDPI, vol. 14(7), pages 1-18, March.

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