基于機器學習的北京市PM2.5濃度預測模型及模擬分析

曲悅; 錢旭; 宋洪慶; 何杰; 李劍輝; 修昊

doi:10.13374/j.issn2095-9389.2019.03.014

基于機器學習的北京市PM2.5濃度預測模型及模擬分析

doi: 10.13374/j.issn2095-9389.2019.03.014

曲悅^{1, 2},
錢旭¹,
宋洪慶^{2, 3, ,},
何杰⁴,
李劍輝^{2, 3},
修昊⁴

1.
中國礦業大學(北京)機電與信息工程學院，北京 100083
2.
大數據分析與計算技術國家地方聯合工程實驗室，北京 100190
3.
北京科技大學土木與資源工程學院，北京 100083
4.
北京科技大學計算機與通信工程學院，北京 100083

基金項目:

中央高校基本科研業務費專項資金資助項目 FRF-TP-17-001C1

北京市科技新星計劃資助項目 Z171100001117081

詳細信息

通訊作者:
宋洪慶, E-mail: songhongqing@ustb.edu.cn

中圖分類號: X831;TP391
計量
- 文章訪問數: 1742
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- PDF下載量: 159
- 被引次數: 0
出版歷程
- 收稿日期: 2018-02-21
- 刊出日期: 2019-03-20

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

QU Yue^{1, 2},
QIAN Xu¹,
SONG Hong-qing^{2, 3
, ,},
HE Jie⁴,
LI Jian-hui^{2, 3},
XIU Hao⁴

1.
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China
2.
National and Local Joint Engineering Lab for Big Data Analysis and Computing Technology, Beijing 100190, China
3.
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
4.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China

More Information

Corresponding author: SONG Hong-qing, E-mail: songhongqing@ustb.edu.cn

摘要

摘要: 對北京市周邊8個點多個壓力高度的溫度、濕度和風速數據, 以及北京市PM2.5污染數據進行了分析和歸一化處理, 建立了反向傳播神經網絡(back propagation, BP)、卷積神經網絡(convolutional neural network, CNN) 和長短期記憶模型(long short-term memory, LSTM) 對上述氣象數據和污染數據進行訓練, 訓練結果表明: 反向傳播神經網絡模型和卷積神經網絡模型對未來1 h的PM2.5污染等級的預測準確率較低, 而長短期記憶模型的準確率較高.使用長短期記憶模型預測未來1 h的PM2.5污染值與實際值十分接近, 表明北京市的PM2.5污染與其周邊地區的氣象條件關系密切.通過利用長短期記憶模型對不同壓力高度的氣象數據進行訓練和對比, 得出在利用氣象數據預測污染時, 僅使用近地面氣象數據比使用多個高度上的氣象數據更加準確.
- 機器學習 /
- PM2.5 /
- 氣象條件 /
- 神經網絡 /
- 長短期記憶 /
- 污染預測
Abstract: In recent years, the air quality in China has become a matter of serious concern. Among the available indicators for evaluating air quality, PM2.5 is one of the most important. It comprises a complex mixture of extremely small particles and liquid droplets emitted into the air, whose diameters are no more than 2.5 μm. Environments with a high PM2.5 index are extremely harmful to human health. Once inhaled, these particles can affect the heart and lungs and cause serious health problems. Air pollution is closely related to meteorological conditions such as wind speed, wind direction, atmospheric stability, temperature, and air humidity. With the development of various machine learning methods, deep learning models based on neural networks are increasingly applied in air pollution research. In this study, the temperature, humidity, wind velocity data at different pressure altitudes from 8 locations around Beijing and average of PM2.5 data in Beijing were analyzed and normalized. Multi-dimensional data was ideal for research applications using machine learning methods. and three neural network models were built, including the back propagation (BP), convolutional neural network (CNN), and long short-term memory (LSTM) models, and trained them using the meteorological and PM2.5 data.The results indicate that the accuracies of the back propagation and convolutional neural network models in predicting the PM2.5 pollution level in the next hour is much lower than that of the long short-term memory model. The PM2.5 pollution index predicted for the next hour by the long short-term memory model is very close to the actual value. This result reveals the strong relationship between the PM2.5 pollution index of Beijing and the local meteorological conditions. The long short-term memory model is trained using meteorological data from different pressure altitudes, and found it to be more accurate in predicting pollution levels when using near-surface meteorological data than that obtained from multiple altitudes.
- machine learning /
- PM2.5 /
- meteorological condition /
- neural networks /
- long short-term memory /
- pollution prediction

HTML全文

圖 1 北京周圍采集的氣象數據點位置

Figure 1. Meteorological data points around Beijing

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圖 3 三層BP神經網絡示意圖

Figure 3. Diagram of three-layer BP neural network

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圖 4 卷積神經網絡結構圖

Figure 4. Convolution neural network structure

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圖 5 LSTM模型中數據在記憶單元中的流動

Figure 5. Flow of data in memory unit in LSTM model

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圖 6 3種方法的訓練與測試損失值

Figure 6. Training and testing losses of three methods

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圖 7 長短期記憶模型對2016年12月PM2.5數值的預測曲線

Figure 7. PM2.5 prediction curve using LSTM model in December 2016

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圖 8 基于一個壓力高度數據的LSTM模型對2016年12月PM2.5數值的預測曲線

Figure 8. PM2.5 prediction curve using LSTM model based on data from one pressure altitude in December 2016

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表 1 空氣質量指數級別與PM2.5對應值

Table 1. Air quality index level and PM2.5 values

空氣質量指數等級	空氣質量指數類別	PM2.5/(mg·m^-3)
一級	優	0~50
二級	良	51~100
三級	輕度污染	101~150
四級	中度污染	151~200
五級	重度污染	201~300
六級	嚴重污染	＞300

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