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Judgment Model of Pollution Source Excessive Emission Based on LightGBM

In: Liss 2022

Author

Listed:
  • Wenhao Ou

    (State Grid Commercial Big Data Co., Ltd)

  • Xintong Zhou

    (State Grid Commercial Big Data Co., Ltd)

  • Zhenduo Qiao

    (State Grid Commercial Big Data Co., Ltd)

  • Liang Shan

    (State Grid Commercial Big Data Co., Ltd)

  • Zhenyu Wang

    (State Grid Commercial Big Data Co., Ltd)

  • Jiayi Chen

    (State Grid Commercial Big Data Co., Ltd)

Abstract

Big data has become a social consensus to improve the modernization level of national governance, support the innovation of government management and social governance modes. This paper aims to strengthen the application of big data in the field of ecological environment. It monitors and analyzes abnormal production behaviors based on energy consumption data. Thus, it assists regulatory authorities to improve supervision efficiency and further protects the legitimate rights of legal enterprises, maintains market fairness, and help the country win the defense of blue sky. By participating in the 5th Digital China Innovation Contest(DCIC), whose topic is ‘Pollution Source Excessive Emission Research and Judgment’, we proposed a multi-feature model for pollution source excessive emission using LightGBM. The final F1 score of model is 0.61203524.

Suggested Citation

  • Wenhao Ou & Xintong Zhou & Zhenduo Qiao & Liang Shan & Zhenyu Wang & Jiayi Chen, 2023. "Judgment Model of Pollution Source Excessive Emission Based on LightGBM," Lecture Notes in Operations Research, in: Xiaopu Shang & Xiaowen Fu & Yixuan Ma & Daqing Gong & Juliang Zhang (ed.), Liss 2022, pages 325-335, Springer.
    • Handle: RePEc:spr:lnopch:978-981-99-2625-1_25
    DOI: 10.1007/978-981-99-2625-1_25
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