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Intelligent Simulation of Water Temperature Stratification in the Reservoir

Author

Listed:
  • Yuan Yao

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

  • Zhenghua Gu

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

  • Yun Li

    (Nanjing Hydraulic Research Institute, Nanjing 210029, China
    State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing 210029, China)

  • Hao Ding

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

  • Tinghui Wang

    (College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China)

Abstract

In order to fully make use of limited water resources, humans have built many water conservancy projects. The projects produce many economic benefits, but they also change the natural environment. For example, the phenomenon of water temperature stratification often occurs in deep reservoirs. Thus, effective ways are needed to predict the water temperature stratification in a reservoir to control its discharge water temperature. Empirical formula methods have low computational accuracy if few factors are considered. Mathematical model methods rely on large amounts of accurate hydrological data and cost long calculation times. The purpose of the research was to simulate water temperature stratification in a reservoir by constructing an intelligent simulation model (ISM-RWTS) with five inputs and one output, determined on the basis of artificial neural networks (ANN). A 3D numerical model (3DNM) was also constructed to provide training samples for the ISM-RWTS and be used to test its simulation effect. The ISM-RWTS was applied to the Tankeng Reservoir, located in the Zhejiang province of China, and performed well, with an average error of 0.72 °C. Additionally, the Intelligent Computation Model of Reservoir Water Temperature Stratification (ICM-RWTS) was also discussed in this paper. The results indicated that the intelligent method was a powerful tool to estimate the water temperature stratification in a deep reservoir. Finally, it was concluded that the advantages of the intelligent method lay in its simplicity of use, its lower demand for hydrological data, its well generalized performance, and its flexibility for considering different input and output parameters.

Suggested Citation

  • Yuan Yao & Zhenghua Gu & Yun Li & Hao Ding & Tinghui Wang, 2022. "Intelligent Simulation of Water Temperature Stratification in the Reservoir," IJERPH, MDPI, vol. 19(20), pages 1-13, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:20:p:13588-:d:947837
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    References listed on IDEAS

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    1. Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
    2. Taymoor Awchi, 2014. "River Discharges Forecasting In Northern Iraq Using Different ANN Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(3), pages 801-814, February.
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