Proceedings of the 4th International Conference on Informatization Economic Development and Management, IEDM 2024, February 23–25, 2024, Kuala Lumpur, Malaysia

Research Article

The Evaluation on Balance of Economic Benefit and Water Pollution of Enterprise Based on Kmeans-RBF Neural Network

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  • @INPROCEEDINGS{10.4108/eai.23-2-2024.2345905,
        author={Minna  Chen and Tiehan  Zhu and Jinlan  Guan},
        title={The Evaluation on Balance of Economic Benefit and Water Pollution of Enterprise Based on Kmeans-RBF Neural Network},
        proceedings={Proceedings of the 4th International Conference on Informatization Economic Development and Management, IEDM 2024, February 23--25, 2024, Kuala Lumpur, Malaysia},
        publisher={EAI},
        proceedings_a={IEDM},
        year={2024},
        month={5},
        keywords={water pollution index system balance degree kmeans-rbf neural network classifier r software},
        doi={10.4108/eai.23-2-2024.2345905}
    }
    
  • Minna Chen
    Tiehan Zhu
    Jinlan Guan
    Year: 2024
    The Evaluation on Balance of Economic Benefit and Water Pollution of Enterprise Based on Kmeans-RBF Neural Network
    IEDM
    EAI
    DOI: 10.4108/eai.23-2-2024.2345905
Minna Chen1, Tiehan Zhu2,*, Jinlan Guan3
  • 1: Guangdong Polytechnic of Environmental Protection Engineering
  • 2: Guangdong Industry Polytechnic
  • 3: Guangdong Agricultural Industry Business Polytechnic
*Contact email: zhuth@126.com

Abstract

According to the requirements of the Ministry of Ecology and Environment of the People’s Republic of China and the evaluation of excellent enterprises, a new evaluation index system for the balance between economic benefits and water pollution protection of enterprises is established. The new indicator system established based on the principles of purposiveness, scientificity, systematicity, and operability has been demonstrated by experts in environmental protection. The index system includes 14 indicators. At the same time, 241 enterprises were selected as the research objects through a focused investigation, and the Kmeans-RBF neural network was constructed using R software. The result matrix of the neural network model was constructed and the results were visualized. The results showed that the classifier had strong stability and effectively classified enterprises into three categories: well balanced enterprises, excellent balanced enterprises, and average balanced enterprises.