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基于长短期记忆网络-卷积神经网络(LSTM-CNN)的北京市PM2.5浓度预测

于伸庭 刘萍

于伸庭, 刘萍. 基于长短期记忆网络-卷积神经网络(LSTM-CNN)的北京市PM2.5浓度预测[J]. 环境工程, 2020, 38(6): 176-180,66. doi: 10.13205/j.hjgc.202006029
引用本文: 于伸庭, 刘萍. 基于长短期记忆网络-卷积神经网络(LSTM-CNN)的北京市PM2.5浓度预测[J]. 环境工程, 2020, 38(6): 176-180,66. doi: 10.13205/j.hjgc.202006029
YU Shen-ting, LIU Ping. LONG SHORT-TERM MEMORY-CONVOLUTION NEURAL NETWORK (LSTM-CNN) FOR PREDICTION OF PM2.5 CONCENTRATION IN BEIJING[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(6): 176-180,66. doi: 10.13205/j.hjgc.202006029
Citation: YU Shen-ting, LIU Ping. LONG SHORT-TERM MEMORY-CONVOLUTION NEURAL NETWORK (LSTM-CNN) FOR PREDICTION OF PM2.5 CONCENTRATION IN BEIJING[J]. ENVIRONMENTAL ENGINEERING , 2020, 38(6): 176-180,66. doi: 10.13205/j.hjgc.202006029

基于长短期记忆网络-卷积神经网络(LSTM-CNN)的北京市PM2.5浓度预测

doi: 10.13205/j.hjgc.202006029
基金项目: 

国家自然科学基金面上项目(41975152)。

详细信息
    作者简介:

    于伸庭(1995-),男,硕士,主要研究方向为大数据分析与数据挖掘。sjtu_yust@163.com

    通讯作者:

    刘萍(1976-),女,博士,主要研究方向为气溶胶形成机制及空气质量的模拟与预测。ping_liu@sjtu.edu.cn

LONG SHORT-TERM MEMORY-CONVOLUTION NEURAL NETWORK (LSTM-CNN) FOR PREDICTION OF PM2.5 CONCENTRATION IN BEIJING

  • 摘要: 准确预测PM2.5浓度可以有效避免重污染天气对人体带来的危害。现有方法往往重视本地历史信息对PM2.5浓度预测的影响,而忽略空间传输的作用。提出了一种长短期记忆网络和卷积神经网络(LSTM-CNN)相结合的方法,利用历史PM2.5浓度数据、历史气象数据和时间数据,对空气质量监测站未来6 h PM2.5浓度做出预测。该模型主要由2部分组成:1)基于长短期记忆网络的时序预测模型,模拟本地因素对PM2.5浓度预测的影响;2)基于一维卷积神经网络的特征提取模型,模拟周边地区污染物的传输与扩散对PM2.5浓度预测的影响。随机选取了北京市市区及郊区7个监测站在2014-05-01—2015-04-30期间的数据,用于研究和评估LSTM-CNN模型。结果表明:提出的LSTM-CNN模型相比于LSTM模型具有更好的预测效果,且对于郊区站点预测效果的改进略优于市区站点。
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  • 收稿日期:  2019-09-25

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