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Water Pollution Prevention and Control Project, 2022, 3(4); doi: 10.38007/WPPCP.2022.030401.

Water Pollution Prevention Engineering Based on K-Means Algorithm and Naive Bayesian Algorithm

Author(s)

Fang Li

Corresponding Author:
Fang Li
Affiliation(s)

Department of Information Engineering, Heilongjiang International University, Harbin 150025, China

Abstract

Water resources are important resources for people’s life and development. However, sewage not only has a huge impact on human health and environment, but also causes a series of negative impacts. In the process of social development, paying attention to the prevention and control of sewage and adopting various effective prevention and control measures can effectively protect water resources and create favorable conditions for the coordinated development of human and nature. Many researchers have provided new ideas for the application of Water Pollution (WP) prevention and control system, and this is the research direction and basis of this paper. This paper analyzed the significance of WP prevention and summarized the research on WP prevention and control engineering and related naive Bayesian algorithm. Then the algorithm model was established, and the relevant algorithms were proposed to study the WP prevention and control engineering based on K-Means algorithm and naive Bayesian algorithm. At the end of the article, the simulation experiment was carried out, and the experiment was summarized and discussed. Firstly, the selected areas were analyzed. The number of WP prevention projects in cities would increase from 2018 to 2021. However, the number of WP prevention and control projects in rural areas has increased from 2018 to 2020, which meant that WP prevention and control work in rural areas was generally easier to carry out than in cities. Through the comparison of natural and social conditions in Region A and B, it was concluded that the difficulty of prevention and control projects was also determined by areas with poor natural original conditions but exceptionally developed social conditions. At the same time, with the in-depth study of K-Means algorithm and naive Bayesian algorithm, the application research of WP prevention engineering is also facing new opportunities and challenges.

Keywords

Water Pollution, Prevention and Control Engineering, Classification Algorithm, Clustering Algorithm

Cite This Paper

Fang Li. Water Pollution Prevention Engineering Based on K-Means Algorithm and Naive Bayesian Algorithm. Water Pollution Prevention and Control Project (2022), Vol. 3, Issue 4: 1-10. https://doi.org/10.38007/WPPCP.2022.030401.

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