IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i10d10.1007_s11269-022-03224-y.html
   My bibliography  Save this article

Study on Water Quality Prediction of Urban Reservoir by Coupled CEEMDAN Decomposition and LSTM Neural Network Model

Author

Listed:
  • Lei Zhang

    (Huazhong University of Science and Technology)

  • Zhiqiang Jiang

    (Huazhong University of Science and Technology)

  • Shanshan He

    (Huazhong University of Science and Technology)

  • Jiefeng Duan

    (Huazhong University of Science and Technology)

  • Pengfei Wang

    (Huazhong University of Science and Technology)

  • Ting Zhou

    (Anhui Agricultural University)

Abstract

Urban reservoir is one of the important urban drinking water sources, and it is of important significance to ensuring the safety of urban water supply. The water quality of the reservoir is an important factor affecting the safety of water supply. Timely and accurate water quality prediction is very important for the formulation of a scientific and reasonable reservoir water supply plan. Considering the problem of high requirement of basic data in constructing water quality hydrodynamic physical model, this paper established a new data-driven model of water quality prediction in urban reservoir based on the Long and Short-Term Memory (LSTM) model, and the water quality data’s decomposition is implemented through the Complete Ensemble Empirical Modal Decomposition with Adaptive Noise (CEEMDAN) method. This model can not only realize the water quality prediction during different foreseen periods, but also solve the problem of low prediction accuracy caused by the randomness and large volatility of the measured data. Taking Xili Reservoir in Shenzhen of China as an example, the prediction of water concentration including total nitrogen, ammonia nitrogen, total phosphorus and PH value of Xili reservoir was realized based on historical monitoring data. Through simulation calculation, the prediction results of total nitrogen, ammonia nitrogen, total phosphorus and PH value in the water quality prediction model are highly consistent with the measured results, it is found that the simulation effect is good, and this model can well simulate the reservoir’s water quality concentration change process. For the total nitrogen and ammonia nitrogen, the relative prediction error of the model can be controlled below 10%, which shows the rationality of the built model. The research of this paper can provide an important theoretical and technical support for the water quality prediction and operation plan formulation of Xili Reservoir.

Suggested Citation

  • Lei Zhang & Zhiqiang Jiang & Shanshan He & Jiefeng Duan & Pengfei Wang & Ting Zhou, 2022. "Study on Water Quality Prediction of Urban Reservoir by Coupled CEEMDAN Decomposition and LSTM Neural Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3715-3735, August.
  • Handle: RePEc:spr:waterr:v:36:y:2022:i:10:d:10.1007_s11269-022-03224-y
    DOI: 10.1007/s11269-022-03224-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03224-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11269-022-03224-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang Pengfei & Jiang Zhiqiang & Duan Jiefeng, 2023. "Burst Analysis of Water Supply Pipe Based on Hydrodynamic Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(5), pages 2161-2179, March.
    2. Peiqiang Gao & Wenfeng Du & Qingwen Lei & Juezhi Li & Shuaiji Zhang & Ning Li, 2023. "NDVI Forecasting Model Based on the Combination of Time Series Decomposition and CNN – LSTM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1481-1497, March.
    3. Yong Huang & Kehan Miao & Xiaoguang Liu & Yin Jiang, 2022. "The Hysteresis Response of Groundwater to Reservoir Water Level Changes in a Plain Reservoir Area," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4739-4763, September.

    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:spr:waterr:v:36:y:2022:i:10:d:10.1007_s11269-022-03224-y. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.