With the continuous development of society, water pollution has become an important factor restricting people’s survival and development, so it is necessary to establish a system that can effectively control water quality, ensure drinking water safety and reduce water waste. Water pollution control means to reduce water consumption, improve water quality and protect the ecological environment by designing an environmental protection measure that can effectively prevent water eutrophication. However, due to the insufficient attention paid to the assessment of water quality, many water bodies have been seriously affected. In order to solve the problems of traditional water pollution control models that rely too much on the subjective judgment and professional knowledge of experts and scholars, such as high difficulty in predicting water pollution problems and low prediction accuracy, based on reality, a stochastic forest algorithm was proposed to build control models to solve the problem. The divided interval samples were collected by computer, and then the samples were randomly classified by random forest algorithm. The calculated average generalization error was reduced by continuously introducing random variables. The final sample classification can more intuitively see the real-time state of water quality. Finally, according to the experimental data, the water pollution control model based on random forest proposed in this paper has an average increase of 13.1% in the four evaluation indicators compared with the traditional water pollution control model.

Roland Szabo. Evaluation on the Construction of Water Pollution Control Model Based on Random Forest. Water Pollution Prevention and Control Project (2020), Vol. 1, Issue 1: 30-39. https://doi.org/10.38007/WPPCP.2020.010104.

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