| 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 |
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| 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. |
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| 作者姓名： | 于伸庭 刘萍 |
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| 作者单位： | 1. 上海交通大学 中英国际低碳学院, 上海 200240; |
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| 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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| 关 键 词： | LSTM网络 CNN网络 PM2.5浓度预测 深度学习 时空数据 |
| 收稿时间： | 2019-09-25 |
LONG SHORT-TERM MEMORY-CONVOLUTION NEURAL NETWORK (LSTM-CNN) FOR PREDICTION OF PM2.5 CONCENTRATION IN BEIJING |
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| 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. |
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| Authors: | YU Shen-ting LIU Ping |
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| Affiliation: | 1. China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 200240, China;2. School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China |
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| Abstract: | The prediction of PM2.5 can effectively prevent people from the harm by heavy pollution. However, the existing methods often emphasize the influence of local historical information and neglect the effect of spatial transport. In this paper, we proposed a method, called as long-short-term memory-convolutional neural network (LSTM-CNN), to predict PM2.5 concentration of a specific air quality monitoring station over 6 h using historical PM2.5 concentration data, historical weather data, and time stamp data. The model consisted of two parts: 1) using long-short-term memory networks to model the local variation of PM2.5 concentrations caused by local factors; 2) using one-dimensional convolutional neural networks to model the variation of PM2.5 concentrations caused by spatial transport. We randomly selected 7 monitoring stations in urban and rural areas in Beijing from May 1st 2014 to April 30th 2015 to conduct the evaluation of LSTM-CNN model. The results showed that the proposed LSTM-CNN model could provide a better prediction result than LSTM model, and a better result for monitoring stations in rural areas than those in urban areas. |
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