Chinese Journal of Intelligent Science and Technology ›› 2021, Vol. 3 ›› Issue (4): 456-465.doi: 10.11959/j.issn.2096-6652.202145

• Papers and Reports • Previous Articles     Next Articles

Dynamic water quality warning with seasonal decomposition and long short-term memory network

Bowen XU1, Jing BI1, Haitao YUAN2, Gongming WANG1, Junfei QIAO1   

  1. 1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    2 School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
  • Revised:2021-03-12 Online:2021-12-15 Published:2021-12-01
  • Supported by:
    The Major Science and Technology Program for Water Pollution Control and Treatment of China(2018ZX07111005);The National Natural Science Foundation of China(61802015);The National Natural Science Foundation of China(62073005);The National Natural Science Foundation of China(62173013)


Surface water quality is increasingly deteriorated in recent years, and therefore, high-quality early warning and prediction of water quality are essential for sustainability of water resources and emergency response mechanisms.Long short-term memory (LSTM) network is widely applied in the existing literature on the prediction of water quality time series.However, only applying LSTM for the prediction of water quality time series cannot well address irregular fluctuations in the water quality series caused by multiple complex factors.To solve this problem, a data-driven prediction model for the water quality time series was proposed, named STL-LSTM-ED, which was composed of seasonal-trend decomposition using locally weighted scatterplot smoothing (STL) and LSTM based on encoder-decoder (LSTM-ED).Compared with several typical models of LSTM, LSTM-ED, and a sequence decomposition method based on LSTM, the proposed STL-LSTM-ED can significantly improve the prediction accuracy and reliability of the water quality time series, and also provide the effective data support for dynamic warning of water quality.

Key words: seasonal decomposition, long short-term memory network, dynamicwarning of water quality, anomaly detection

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