Due to the complexity of water pollution treatment and the impact of external factors, precise regulation of water pollution has always been one of the problems in the environmental field. Traditional methods cannot meet the increasingly complex engineering project requirements. Machine learning methods developed in recent years provide a series of effective solutions for such problems. Taking the Xiangjiang River basin as an example, this paper introduces the characteristics of machine learning methods such as artificial neural network, support vector machine and random forest, and expounds the application of machine learning methods in the field of water pollution control in the Xiangjiang River basin from three aspects of water quality prediction and early warning, fault diagnosis of sewage treatment system and intelligent control, and analyzes the advantages of machine learning methods compared with traditional methods and the problems existing in the application of machine learning methods in water pollution control in the Xiangjiang River basin, the prospect and trend of the application of machine learning methods in the field of water pollution control in the Xiangjiang River basin are forecasted.

Yu Yuan, Dazhi Xu. Research on the Water Pollution Control Mechanism of Xiangjiang River Basin Based on Machine Learning. Academic Journal of Environment & Earth Science (2022) Vol. 4 Issue 8: 48-52. https://doi.org/10.25236/AJEE.2022.040809.

[1] Qian W.J., He C.F. China’s regional difference of water resource use efficiency and influencing factors[J]. China Population, Resources and Environment, 2011, 21(2):54-60.

[2] Sun Z.L., Sun H., Su Y. Water use efficiency and its influencing factors in arid areas of Northwest China[J]. Journal of Ecology and Rural Environment, 2017, 33(11):961-967.

[3] Xu X.Y., Wang H.R. Liu H.J., et. Al. Evaluation report on water resources utilization efficiency in China[M]. Beijing: Beijing Normal University Press, 2010

[4] Song G.J., He W. Benchmarking of city water resource utilization efficiency in China[J]. Resources Science, 2014, 36(12): 2569-2577.

[5] Jamaluddin M Y, David H. The efficiency of the National Electricity Board in Malaysia: An intercountry comparison using DEA[J]. Energy  Economics,1997,19(1):255-269 .

[6] Mai Y.Z., Sun F.L., Shi L., et.al. Evaluation of China’s industrial water efficiency based on DEA model[J].Journal of Arid Land Resources and Environment, 2014,28(11):42-47.

[7] Zhao L.S., Sun C.Z., Liu F.C. Two-stage utilization efficiency of the interprovincial water resources under environmental constraint and its influence factors in China[J]. China Population, Resources and Environment, 2017, 27(5):27-36. 

[8] Dong Z.F., Yu E.Y., Qiu L., et al. Water efficiency evaluation of the provincial regions in China based on DEA model[J]. Ecological Economy, 2012(10):43-47. 

[9] Hu L.K. Dynamic evaluation of water resources utilization efficiency in Yunnan province based on WCA-MEPP model[J]. Journal of Water Resources & Water Engineering, 2017,28(4):75-87+81. 

[10] Tony A. Productive Efficiency and Allocative Efficiency: Why Better Water Management Might not Solve the Problem [J]. Agricultural Water Management, 1999, 40(3):71-75.

[11] Anwandter L. Can Public Sector Reforms Improve the Efficiency of Public Water Utilities an Empirical Analysis of the Water Sector in Mexico Using Data Envelopment Analysis[D]. Maryland: University of Maryland, 2000.

[12] Wang X.Y., Zhao L.G. Agricultural water efficiency and the causal factors——A stochastic frontier analysis based on Chinese provincial panel data: 1997-2006[J]. Issues in Agricultural Economy, 2008, 29(3): 10–18.