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Wu, J.H. and Wang, Z.C. (2022) A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory. Water, 14, Article 610.
https://doi.org/10.3390/w14040610

has been cited by the following article:

• TITLE: Research on Water Quality Parameter Prediction Model Based on TCN

  AUTHORS: Hang Yang, Xiaoqi Yin, Li Hong, Kun Jiang, Xingyu Zhang

  KEYWORDS: Water Quality Parameter Prediction, Neural Network, Time Convolution

  JOURNAL NAME: Open Access Library Journal, Vol.11 No.4, April 30, 2024

  ABSTRACT: Water quality parameter prediction is a new water quality detection technology in the era of big data, which plays a vital role in the management and protection of water resources. Traditional water quality parameter prediction models such as RNN and LSTM have low accuracy and cannot provide scientific basis for water environment management effectively. This paper takes the water quality parameters of Subei irrigation canal in Huaian as the research object, analyzes and compares three prediction methods: RNN, LSTM and time convolutional neural network (TCN). The experimental results show that TCN is 0.0237418, 0.1326965 and 0.9891232 under MSE, MAE and R2 indexes, respectively. Compared with RNN and LSTM, the prediction accuracy is greatly improved, and the change trend of water quality parameters can be effectively predicted.