China is facing a serious situation in water pollution prevention and control, and the uncertainty of regional development should be fully considered when formulating water pollution prevention and control planning, and its scientificity should be improved through effective mathematical methods. Water resources and water environment are closely related to human health, and China has not yet established a comprehensive set of water resources and environment monitoring network, so the water pollution prevention and control planning process is crucial to the implementation of water pollution control planning. Due to the relatively low level of economic development in China, the state attaches less importance to water pollution prevention and control issues, and therefore also has a certain impact on the water environment. In order to reduce the problems caused by water pollution, the Water Resources Management Committee has organised the preparation of water pollution control plans. This paper presents an empirical analysis of the support vector model for water pollution prevention and control planning in China. The results of the study show that the introduction of the support vector approach to water pollution prevention and control planning in China can effectively improve the scientific nature of the planning. This paper firstly introduces the support vector model, which is a multivariate model designed based on the support vector method, and can be used to analyse multiple indicators and conduct comprehensive analysis in the prediction process; finally, the planning scheme should be designed with full consideration of water pollution prevention and control planning to avoid conflicts between different water resources and environmental issues. However, there are many problems with the use of the support vector approach in water pollution prevention and control planning that warrant further research.

Lizunov Sae. Linear Regression for Water Pollution Control Planning under Support Vectors. Water Pollution Prevention and Control Project (2021), Vol. 2, Issue 3: 51-60. https://doi.org/10.38007/WPPCP.2021.020306.

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