With the high-quality development of the current social economy, people's demand for various resources in nature is also increasing. This increasing demand has led to the abuse of various natural resources in some regions, which not only caused the waste of ecological resources, but also caused more damage to the ecological environment. Therefore, with the further development of various technologies and economies, government agencies in most regions began to attach importance to the rational use of natural resources, prevention and control of ecological pollution and other work. Most of the current pollution treatment and control models are based on the previous technical fields, and do not cite some information technologies that have emerged recently. Therefore, in the current pollution control, the work process is often more complex, and the work efficiency can not meet people's expectations. In addition, with the further aggravation of social ecological environment pollution, conventional pollution control measures can no longer meet the rapid growth of social development. At this time, the emergence of artificial intelligence (AI) also brings new ideas to the means of pollution control of the ecological environment. Reconstruction of the existing water resources management and control system through AI technology can not only further improve its performance, but also greatly improve the efficiency of water pollution prevention. This paper first analyzed the existing water pollution prevention and control (WPPC for short here) mode, and identified some shortcomings in the existing prevention and control mode. Then, the existing prevention and control mode were reconstructed by AI technology and support vector machines (SVM) algorithm. Finally, through simulation experiments, the performance difference between the existing WPPC model and the new model proposed in this paper in multiple evaluation indicators was determined, and the new model proposed in this paper has increased by about 25.1% compared with the existing prevention and control model on average.

Dorin Copaci. Progress of Artificial Intelligence Applied in Water Pollution Prevention and Control Process. Water Pollution Prevention and Control Project (2020), Vol. 1, Issue 1: 11-19. https://doi.org/10.38007/WPPCP.2020.010102.

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