With the continuous progress of industrialization, the social economy has shown a rapid growth trend, and the living sewage discharge has also increased, which has led to a decline in the quality of the surface water environment and harmed human health. Water quality has become a major factor restricting the sustainable development of economy and society. In view of the frequent occurrence of water pollution accidents and the deterioration of water environment, people pay more and more attention to the prevention and protection of water resources. However, the traditional manual control method cannot effectively solve the problem of water pollution, so it is necessary to innovate the existing technology. Automatic control technology and information technology can be combined to realize online monitoring of pollutants, so as to achieve real-time monitoring and timely warning. Support Vector Machine (SVM) is an adaptive processing method based on machine learning algorithm. By training the neural network, more accurate results can be obtained. To some extent, it overcomes the shortcomings brought by manual operation and has a broad application prospect in the field of environmental monitoring. This paper combined SVM and artificial neural network to develop a set of water quality automatic monitoring system, and used SVM model to predict sewage concentration, which also combined Back Propagation (BP) network to optimize parameters to improve detection accuracy. Finally, by strengthening the application of technology, a scientific and effective evaluation system was established to achieve the goal of water pollution prevention and control. Compared with traditional prevention and control methods, intelligent water environment treatment based on SVM model could quickly and accurately identify the water quality status, and the work efficiency was also improved by 19.35%. It could quickly and accurately determine whether there were toxic and harmful pollutants in the sewage, thus ensuring the health of the people and reducing the occurrence of environmental pollution events, which provided strong support for environmental protection.

Yannis L. Karnavas. Simulation of Water Pollution Control Engineering Based on Support Vector Machine Model. Water Pollution Prevention and Control Project (2020), Vol. 1, Issue 4: 11-20. https://doi.org/10.38007/WPPCP.2020.010402.

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