PREDICTION OF TOTAL PHOSPHORUS IN RIVERS BASED ON ATTENTION MECHANISM OF TEMPORAL CONVOLUTIONAL NETWORKS

LI Yuanyuan; LIU Hailong

doi:10.13205/j.hjgc.202305022

Volume 41 Issue 5

May 2023

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(5): 163-171

LIU Yu-long, ZHANG Zhi-feng, ZHANG Li, ZHANG Zhe, QIN Lu, CHAI Guo-dong, ZHENG Xing, WANG Dong-qi. EFFECT OF INFLUENT CONDITIONS ON PERFORMANCE AND MICROORGANISMS IN THE SIDE-STREAM ACTIVATED SLUDGE HYDROLYSIS PROCESS[J]. ENVIRONMENTAL ENGINEERING , 2022, 40(5): 146-151,158. doi: 10.13205/j.hjgc.202205021

Citation:

LI Yuanyuan, LIU Hailong. PREDICTION OF TOTAL PHOSPHORUS IN RIVERS BASED ON ATTENTION MECHANISM OF TEMPORAL CONVOLUTIONAL NETWORKS[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(5): 163-171. doi: 10.13205/j.hjgc.202305022

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PREDICTION OF TOTAL PHOSPHORUS IN RIVERS BASED ON ATTENTION MECHANISM OF TEMPORAL CONVOLUTIONAL NETWORKS

doi: 10.13205/j.hjgc.202305022

LI Yuanyuan,
LIU Hailong^,

School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China

Received Date: 2022-07-26

Abstract

Abstract

The application of deep learning in water pollution monitoring has become a current hot topic. Taking the total phosphorus prediction of Min River water system in Pengshan District, Meishan City, Sichuan Province as the research object, a temporal convolutional network (TCN) prediction model based on temporal pattern attention (TPA) mechanism was proposed to solve the problem that the concentration of water pollutants was related to different time steps between upstream and downstream. Firstly, the Pearson correlation coefficient was applied to analyze the spatiotemporal relationship of total phosphorus concentration at upstream and downstream sites. Then, the dilated convolution and causal convolution of TCN were used to extract the dependencies of the time series data. And the TPA mechanism was used to learn the complex spatial relationship between the time series of total phosphorus concentration at different stations from TCN, which can obtain the station weights at different time steps in the time series data. Finally, this model was applied to predict the total phosphorus concentration of rivers. The research results showed that the multi-site input TPA-TCN model reduced RMSE by 36.29%, MAE by 28.18% and MAPE by 25.26%, compared with that of the single-site input TPA-TCN model. Compared with TCN model integrated with the traditional attention mechanism, the three evaluation metrics of TCN model integrated with TPA mechanism reduced by 10.24%, 10.78% and 9.94%, indicating that the multi-site input TPA-TCN model has certain advantages in predicting the total phosphorus concentration in rivers. The TPA-TCN model can be effectively applied to water quality monitoring, which has important reference significance for the prediction of water pollutants.
- water quality prediction,
- temporal convolutional network,
- temporal pattern attention mechanism,
- Pearson correlation coefficient

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References(33)

References

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