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基于机器学习的北京市PM2.5浓度预测模型及模拟分析

曲悦 钱旭 宋洪庆 何杰 李剑辉 修昊

曲悦, 钱旭, 宋洪庆, 何杰, 李剑辉, 修昊. 基于机器学习的北京市PM2.5浓度预测模型及模拟分析[J]. 工程科学学报, 2019, 41(3): 401-407. doi: 10.13374/j.issn2095-9389.2019.03.014
引用本文: 曲悦, 钱旭, 宋洪庆, 何杰, 李剑辉, 修昊. 基于机器学习的北京市PM2.5浓度预测模型及模拟分析[J]. 工程科学学报, 2019, 41(3): 401-407. doi: 10.13374/j.issn2095-9389.2019.03.014
QU Yue, QIAN Xu, SONG Hong-qing, HE Jie, LI Jian-hui, XIU Hao. Machine-learning-based model and simulation analysis of PM2.5 concentration prediction in Beijing[J]. Chinese Journal of Engineering, 2019, 41(3): 401-407. doi: 10.13374/j.issn2095-9389.2019.03.014
Citation: QU Yue, QIAN Xu, SONG Hong-qing, HE Jie, LI Jian-hui, XIU Hao. Machine-learning-based model and simulation analysis of PM2.5 concentration prediction in Beijing[J]. Chinese Journal of Engineering, 2019, 41(3): 401-407. doi: 10.13374/j.issn2095-9389.2019.03.014

基于机器学习的北京市PM2.5浓度预测模型及模拟分析

doi: 10.13374/j.issn2095-9389.2019.03.014
基金项目: 

北京市科技新星计划资助项目(Z171100001117081)

中央高校基本科研业务费专项资金资助项目(FRF-TP-17-001C1)

详细信息
  • 中图分类号: X831;TP391

Machine-learning-based model and simulation analysis of PM2.5 concentration prediction in Beijing

  • 摘要: 对北京市周边8个点多个压力高度的温度、湿度和风速数据,以及北京市PM2.5污染数据进行了分析和归一化处理,建立了反向传播神经网络(back propagation,BP)、卷积神经网络(convolutional neural network,CNN)和长短期记忆模型(longshort-term memory,LSTM)对上述气象数据和污染数据进行训练,训练结果表明:反向传播神经网络模型和卷积神经网络模型对未来1 h的PM2.5污染等级的预测准确率较低,而长短期记忆模型的准确率较高.使用长短期记忆模型预测未来1 h的PM2.5污染值与实际值十分接近,表明北京市的PM2.5污染与其周边地区的气象条件关系密切.通过利用长短期记忆模型对不同压力高度的气象数据进行训练和对比,得出在利用气象数据预测污染时,仅使用近地面气象数据比使用多个高度上的气象数据更加准确.
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  • 收稿日期:  2018-02-21

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