Abstract
Water quality prediction is of great significance for the supervision of water environment. At present, artificial intelligence method has been tried to be introduced into this field. In this paper, a novel spatial-temporal convolutional attention network based on residual correction and parameter optimization, is proposed for water quality prediction. The model can be divided into three parts. The first part is convolutional attention network in spatial-temporal domain, which uses an one-dimensional convolutional network to extract temporal and spatial information of water quality monitoring station, and adds attention mechanism; the second part is TCN residual correction module, which corrects the residual of the first part; the third part is the parameter optimization module, which introduces PSO algorithm to optimize the model parameters of the first two parts to obtain better results. Based on the real water quality data of a river in South China, this paper carries out relevant comparative experiments, and the experimental results show that the water quality prediction model proposed in this paper is better than other models.
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Acknowledgement
This paper is supported by National Key R&D Program of China (2018YFB1004 300).
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Yu, X., Peng, W., Xue, D., Ni, Q. (2021). An Improved Spatial-Temporal Network Based on Residual Correction and Evolutionary Algorithm for Water Quality Prediction. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12690. Springer, Cham. https://doi.org/10.1007/978-3-030-78811-7_46
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DOI: https://doi.org/10.1007/978-3-030-78811-7_46
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