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?
Elham Kalantari1, Hamid Gholami2, Hossein Malakooti3
1Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.
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.
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.
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