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机器学习用于空气质量指数 (AQI) 预测:浅层学习还是深度学习?

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  • 1Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.

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深度学习模型,特别是CNN,在预测空气质量指数 (AQI) 和分类污染水平方面表现出色,优于传统的机器学习. 统一的数据收集对于准确的空气污染预测至关重要.

关键词:
空气质量指数是指空气质量指数.深度学习是一种深度学习.功能选择 功能选择机器学习 机器学习扎博尔扎博尔 Zabol Zabol Zabol Zabol Zabol Zabol Zabol Zabol Zabol Zabol Zabol Zabol Zabol Zabol Zabol Zabol Zabol Zabol Zab

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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
  • 大气科学 大气科学

背景情况:

  • 空气污染对公众健康和环境构成重大风险.
  • 准确的空气质量预测和分类对于有效的缓解策略至关重要.
  • 机器学习为分析复杂的环境数据提供了强大的工具.

研究的目的:

  • 评估和比较各种浅层学习 (SL) 和深度学习 (DL) 模型用于空气质量指数 (AQI) 预测和分类.
  • 用PM10度和气象数据确定最有效的ML模型来预测空气污染.
  • 使用准确性,F1得分,精度,回忆和AUC等指标来评估模型性能.

主要方法:

  • 使用了一系列ML模型,包括随机森林,KNN,SVM,ANN,LSTM,GRU,RNN和CNN.
  • 利用每日PM10度和9个气象参数从扎博尔 (2013年3月至2022年2月).
  • 应用信息获取 (IG) 方法来选择特征,并计算多个性能指标用于模型评估.

主要成果:

  • 深度学习模型,特别是CNN,在AQI预测和分类方面表现出卓越的表现.
  • CNN获得了最高的精度 (0.60),紧随其后的是RF (0.58).
  • DL模型在分类空气质量水平方面实现了高AUC值 (例如,0.95为"好",0.90为"危险"),显著优于SL模型.

结论:

  • 深度学习模型,特别是CNN,对于复杂的空气质量分类和预测非常有效.
  • 这些发现为制定有针对性的空气污染控制战略提供了宝贵的见解.
  • 持续和定期收集空气质量数据对于提高预测模型可靠性至关重要.