Prediction of PM2.5 Daily Concentration of Guangzhou Based on Neural Network Algorithms

被引：0

作者：

Li Z. ^{[1
]}

Wei J. ^{[1
,2
,3
]}

机构：

[1] School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai

[2] Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai

[3] School of Marine Sciences, Guangxi University, Nanning

来源：

Beijing Daxue Xuebao (Ziran Kexue Ban)/Acta Scientiarum Naturalium Universitatis Pekinensis | 2021年 / 57卷 / 04期

关键词：

Autoregressive integrated moving average model (ARIMA); Back propagation (BP); Ensemble empirical mode decomposition (EEMD); Guangzhou city; Long short-term memory networks (LSTM); PM[!sub]2.5[!/sub;

D O I：

10.13209/j.0479-8023.2021.050

中图分类号：

学科分类号：

摘要：

Autoregressive integrated moving average (ARIMA) model, back propagation (BP) neutral network and long short-term memory (LSTM) are used to predict the daily concentration of PM2.5 in 2019 in Guangzhou city of China from 2015 to 2019. The effect of ensemble empirical mode decomposition (EEMD), temporal resolution on model prediction is explored in this paper. The results show that EEMD is able to improve significantly the prediction ability of the model on the low-frequency part of PM2.5 sequence. Increased temporal resolution can improve the prediction accuracy, with more input data. Since PM2.5 (t-1) is used as the input data, the model can only predict PM2.5 for 1 day in advance. To increase the prediction time window, we adopt a rolling forecast method, using PM2.5 (t) prediction value as the input data for PM2.5 (t+1). The result shows that the rolling forecast method allows the model to forecast PM2.5 (t+n) with a comparable MAE compared to the experiment without the rolling forecast method. In this paper, the ARIMA model (the time accuracy of input data is 6 hours) has the best prediction accuracy, and the minimum MAE value can reach 6.478. © 2021 Peking University.

引用

页码：645 / 652

页数：7

共 12 条

[1] Lelieveld J, Evans J S, Fnais M, Et al., The contribution of outdoor air pollution sources to premature mortality on a global scale, Nature, 525, pp. 367-371, (2015)
[2] Li Jin, Liu Huan, Lv Zhaofeng, Et al., Estimation of PM2.5 mortality burden in China with new exposure estimation and local concentration-response function, Environmental Pollution, 243, pp. 1710-1718, (2018)
[3] Baker K R, Foley K M., A nonlinear regression model estimating single source concentrations of primary and secondarily formed PM2.5, Atmospheric Environ-ment, 45, 22, pp. 3758-3767, (2011)
[4] 6
[5] 6, (2014)
[6] 3
[7] Lu Xingcheng, Lin Changqing, Li Ying, Et al., Assessment of health burden caused by particulate matter in southern China using high-resolution satellite observation, Environment International, 98, pp. 160-170, (2017)
[8] Huang N E, Shen Z, Long S R, Et al., The empirical mode decomposition and the hilbert spectrum for non-linear and non-stationary time series analysis, Proceedings Mathematical Physical & Engineering Sciences, 454, pp. 903-995, (1998)
[9] Wu Z H, Huang N E., Ensemble empirical mode decomposition: a noise-assisted data analysis method, Advances in Adaptive Data Analysis, 1, 1, pp. 1-41, (2009)
[10] Hochreiter S, Schmidhuber J., Long short-term memo-ry, Neural computation, 9, 8, pp. 1735-1780, (1997)

← 1 2 →