With the rapid development of modern science and industrial technology, people’s material living standards have been greatly improved, and at the same time, the social economy has been developed with higher quality. However, in this early stage of development, enterprises and residents in some regions have not yet had a good awareness of environmental protection, which leads to a rapid increase in the degree of environmental pollution in the process of social and economic development. On the other hand, water resources have always been the most basic but also the most important part of the ecological environment, and water resources are also polluted by various industrial wastes in the process of rapid industrial development. This kind of pollution not only makes the water resources in some areas gradually exhausted, but also brings about soil erosion and water pollution problems. Pure water resources have always been a necessity for the development of people and society. However, the pollution of water resources in the process of disorderly industrial development makes it difficult for people to obtain pure water resources easily. Therefore, it is necessary to optimize and upgrade the existing water pollution prevention and control measures. This optimization and upgrading is not only the update of the existing water pollution treatment technology, but also the simplification of the work flow in the existing water pollution prevention and control measures, so as to significantly improve the work efficiency of the water pollution prevention and control measures and provide better support for the residents’ life and social economy. First of all, this paper analyzed the support vector machines (SVM) algorithm in artificial intelligence (AI) technology, and determined the various models of this SVM algorithm model applied in current real life, so as to study whether the SVM algorithm can be applied in the optimization of water pollution prevention measures. After that, based on the SVM algorithm model, a new type of water pollution prevention and control measure was constructed, and the performance of this new type of water pollution prevention and control measure was improved by about 23.9% on average.

Garg Sahil. Support Vector Machine Algorithm in Water Pollution Prevention Measures. Water Pollution Prevention and Control Project (2022), Vol. 3, Issue 2: 12-20. https://doi.org/10.38007/WPPCP.2022.030202.

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