Author(s)

Hang Yang, Xiaoqi Yin^*, Li Hong, Kun Jiang, Xingyu Zhang

Huaiyin Institute of Technology, Huai’an, China.

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.

Water Quality Parameter Prediction, Neural Network, Time Convolution

Yang, H. , Yin, X. , Hong, L. , Jiang, K. and Zhang, X. (2024) Research on Water Quality Parameter Prediction Model Based on TCN. Open Access Library Journal, 11, 1-10. doi: 10.4236/oalib.1111421.