Data driven theory-based optimization emission reduction model for river pollution

LIU Jie; YANG Kai-peng; GE Qin; LI Xiao-yu; YANG Jia-le; XI Dong; JIANG De-xun; LI Mo

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China Environmental Science ›› 2026, Vol. 46 ›› Issue (1) : 188-198.

Water Pollution Control

Data driven theory-based optimization emission reduction model for river pollution

LIU Jie¹, YANG Kai-peng¹, GE Qin¹, LI Xiao-yu¹, YANG Jia-le², XI Dong³, JIANG De-xun³, LI Mo¹

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Abstract

In order to improve the water pollution control level in the basin and solve the shortcomings of existing optimization emission reduction technologies, a data driven theory-based optimization emission reduction model for river pollution was developed with the combination of Bidirectional Long and Short Memory Network (Bi-LSTM) algorithm and Bayesian Optimization (BO) algorithm considering the receiving water body as the research object. The developed optimization emission reduction model was then applied to Songhua River basin from Baidu Town and Sandao to Hongkeli section. The results show that the Bi-LSTM algorithm can effectively train the spatial neural network architecture, and improve the study accuracy of water quality spatial relationship. The reduction rates of TN in Baidu Town and Sandao are [14.12%, 38.84%] and [15.01%, 38.98%], respectively, and TP hardly needs to be reduced under the water quality standard III. While the reduction rates of TN and TP in Baidu Town are [19.08%, 39.72%] and [0.00%, 41.93%], and the ones in Sandao are [18.43%, 40.09%] and [0.00%, 36.24%], respectively under the water quality standard II. The optimal reduction strategies can be obtained by the developed optimization emission reduction model under different reduction scenarios to provide decision support for accurate reduction and intelligent control of river pollution.

Key words

river pollution / precise emission reduction / intelligent control / bidirectional long and short memory network / bayesian optimization

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LIU Jie, YANG Kai-peng, GE Qin, LI Xiao-yu, YANG Jia-le, XI Dong, JIANG De-xun, LI Mo. Data driven theory-based optimization emission reduction model for river pollution[J]. China Environmental Science. 2026, 46(1): 188-198

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