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基于移动窗口的机器学习用于环境检查 资源分配

Qi Zhou1,2, Shen Qu1,2, Qianzi Wang1,2

  • 1School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China.

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概括
此摘要是机器生成的。

机器学习模型预测企业环境检查失败,改善资源配置. 这种数据驱动的方法提高了监管的有效性,并促进了可持续发展.

关键词:
数据驱动的数据驱动.环境管理环境管理检查分配的检查分配机器学习 机器学习

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科学领域:

  • 环境科学 环境科学
  • 数据科学数据科学数据科学
  • 监管科学 监管科学

背景情况:

  • 环境法规面临资源限制,限制了检查的有效性.
  • 可持续发展需要有效监督工业合规性.
  • 预测建模可以优化环境检查的资源配置.

研究的目的:

  • 开发和评估用于预测企业检查失败的机器学习模型.
  • 为环境监管机构优化资源分配策略.
  • 通过使用数据驱动的方法提高环境检查的有效性.

主要方法:

  • 使用了四个基于滑动窗口的机器学习技术.
  • 采用了来自16777家化学企业 (2010-2021) 的特征工程时间序列数据.
  • 与长短期内存 (LSTM) 模型进行模型性能比较,实现ROC AUC > 0.83.

主要成果:

  • 机器学习模型在预测检查失败方面表现出很高的准确性.
  • 最近的违规历史会对未来的违规行为产生重大影响.
  • 提出了基于风险的资源分配场景,其中一个增加了检测率的8倍以上.

结论:

  • 滑窗机器学习为环境监管提供了对深度学习的可行替代方案.
  • 数据驱动的洞察力能够优化检查资源的分配.
  • 加强监管效率有助于实现可持续发展目标.