Air Pollutant Concentration Forecasting Using Long Short-Term Memory Based on Wavelet Transform and Information Gain: : A Case Study of Beijing
Authors: 
Bingchun Liu, Xiaoling Guo, Mingzhao Lai, Qingshan Wang Academic Editor: Cornelio Yáñez-MárquezAuthors Info & Claims
Bingchun Liu, Xiaoling Guo, Mingzhao Lai, Qingshan Wang Academic Editor: Cornelio Yáñez-MárquezAuthors Info & ClaimsAbstract
Air pollutant concentration forecasting is an effective way which protects health of the public by the warning of the harmful air contaminants. In this study, a hybrid prediction model has been established by using information gain, wavelet decomposition transform technique, and LSTM neural network, and applied to the daily concentration prediction of atmospheric pollutants (PM2.5, PM10, SO2, NO2, O3, and CO) in Beijing. First, the collected raw data are selected by feature selection by information gain, and a set of factors having a strong correlation with the prediction is obtained. Then, the historical time series of the daily air pollutant concentration is decomposed into different frequencies by using a wavelet decomposition transform and recombined into a high-dimensional training data set. Finally, the LSTM prediction model is trained with high-dimensional data sets, and the parameters are adjusted by repeated tests to obtain the optimal prediction model. The data used in this study were derived from six air pollution concentration data in Beijing from 1/1/2014 to 31/12/2016, and the atmospheric pollutant concentration data of Beijing between 1/1/2017 and 31/12/2017 were used to test the predictive ability of the data set test model. The results show that the evaluation index MAPE of the model prediction is 7.45%. Therefore, the hybrid prediction model has a higher value of application for atmospheric pollutant concentration prediction, because this model has higher prediction accuracy and stability for future air pollutant concentration prediction.
References
[1]
N. Künzli, R. Kaiser, S. Medina et al., “Public-health impact of outdoor and traffic-related air pollution: a European assessment,” Lancet (North American Edition), vol. 356, no. 9232, pp. 795–801, 2000.
[2]
D. Chen, X. Liu, J. Lang et al., “Estimating the contribution of regional transport to PM 2.5 air pollution in a rural area on the North China Plain,” Science of The Total Environment, vol. 583, pp. 280–291, 2017.
[3]
Y. Zhang, M. Bocquet, V. Mallet, C. Seigneur, and A. Baklanov, “Real-time air quality forecasting, part I: history, techniques, and current status,” Atmospheric Environment, vol. 60, pp. 632–655, 2012.
[4]
G. Gualtieri, F. Carotenuto, S. Finardi et al., “Forecasting PM10 hourly concentrations in northern Italy: insights on models performance and PM10 drivers through self-organizing maps,” Atmospheric Pollution Research, vol. 9, no. 6, pp. 1309–1042, 2018.
[5]
W. Sun and J. Sun, “Daily PM2.5 concentration prediction based on principal component analysis and LSSVM optimized by cuckoo search algorithm,” Journal of Environmental Management, vol. 188, pp. 144–152, 2016.
[6]
A. B. Sánchez, C. Ordóñez, F. S. Lasheras, F. J. d. C. Juez, and J. Roca-Pardiñas, “Forecasting SO2 pollution incidents by means of Elman artificial neural networks and ARIMA models,” Abstract and Applied Analysis, vol. 2013, 6 pages, 2013.
[7]
Q. Z. Wu, W. S. Xu, A. J. Shi et al., “Air quality forecast of PM10 in Beijing with community multi-scale air quality modeling (CMAQ) system: emission and improvement,” Geoscientific Model Development, vol. 7, no. 5, pp. 2243–2259, 2014.
[8]
S. P. Das and S. Padhy, “A novel hybrid model using teaching–learning-based optimization and a support vector machine for commodity futures index forecasting,” International Journal of Machine Learning & Cybernetics, vol. 9, no. 1, pp. 97–111, 2018.
[9]
M. A. Elangasinghe, J. A. Salmond, K. N. Dirks, and J. A. Salmond, “Development of an ANN-based air pollution forecasting system with explicit knowledge through sensitivity analysis,” Atmospheric Pollution Research, vol. 5, no. 4, pp. 696–708, 2014.
[10]
Q. Zhou, H. Jiang, J. Wang, and J. Zhou, “A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network,” Science of The Total Environment, vol. 496, pp. 264–274, 2014.
[11]
P. Wang, Y. Liu, Z. Qin, and G. Zhang, “A novel hybrid forecasting model for PM10 and SO2 daily concentration,” Science of the Total Environment, vol. 505, no. 505C, pp. 1202–1212, 2015.
[12]
P. Li, R. Yan, S. Yu, S. Wang, W. Liu, and H. Bao, “Reinstate regional transport of PM2.5 as a major cause of severe haze in Beijing,” Proceedings of the National Academy of Sciences, vol. 112, no. 21, pp. E2739–E2740, 2015.
[13]
B. Lv, W. G. Cobourn, and Y. Bai, “Development of nonlinear empirical models to forecast daily PM2.5 and ozone levels in three large Chinese cities,” Atmospheric Environment, vol. 147, pp. 209–223, 2016.
[14]
X. Y. Ni, H. Huang, and W. P. Du, “Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data,” Atmospheric Environment, vol. 150, pp. 146–161, 2017.
[15]
B.-C. Liu, A. Binaykia, P.-C. Chang, M. K. Tiwari, and C.-C. Tsao, “Urban air quality forecasting based on multi-dimensional collaborative support vector regression (SVR): a case study of Beijing-Tianjin-Shijiazhuang,” PLoS One, vol. 12, no. 7, 2017.
[16]
D. Wang, S. Wei, H. Luo, C. Yue, and O. Grunder, “A novel hybrid model for air quality index forecasting based on two-phase decomposition technique and modified extreme learning machine,” Science of The Total Environment, vol. 580, pp. 719–733, 2017.
[17]
E. Habler and A. Shabtai, “Using LSTM encoder-decoder algorithm for detecting anomalous ADS-B messages,” Computers & Security, vol. 78, pp. 155–173, 2018.
[18]
Y. Wu, M. Yuan, S. Dong, L. Lin, and Y. Liu, “Remaining useful life estimation of engineered systems using vanilla LSTM neural networks,” Neurocomputing, vol. 275, no. 31, pp. 167–179, 2018.
[19]
J. Zhang, Z. Yan, Z. Xiaoping, Y. Ming, and Y. Jinzhong, “Developing a long short-term memory (LSTM) based model for predicting water table depth in agricultural areas,” Journal of Hydrology, vol. 561, pp. 918–929, 2018.
[20]
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.
[21]
H. Luo, M. Huang, and Z. Zhou, “Integration of Multi-Gaussian fitting and LSTM neural networks for health monitoring of an automotive suspension component,” Journal of Sound and Vibration, vol. 428, pp. 87–103, 2018.
[22]
H. Y. Kim and C. H. Won, “Forecasting the volatility of stock price index: a hybrid model integrating LSTM with multiple GARCH-type models,” Expert Systems with Applications, vol. 103, no. 1, pp. 25–37, 2018.
[23]
M. J. Alizadeh and M. R. Kavianpour, “Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean,” Marine Pollution Bulletin, vol. 98, no. 1-2, pp. 171–178, 2015.
[24]
Y. Chen, S. Runhe, S. Shijie, and G. Wei, “Ensemble and enhanced PM10 concentration forecast model based on stepwise regression and wavelet analysis,” Atmospheric Environment, vol. 74, pp. 346–359, 2013.
[25]
V. Nourani, A. H. Baghanam, J. Adamowski, and K. Ozgur, “Applications of hybrid wavelet–Artificial Intelligence models in hydrology: a review,” Journal of Hydrology, vol. 514, pp. 358–377, 2014.
[26]
X. Feng, Q. Li, Z. Yajie et al., “Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation,” Atmospheric Environment, vol. 107, pp. 118–128, 2015.
[27]
A. Vlachogianni, P. Kassomenos, A. Karppinen, S. Karakitsios, and J. Kukkonen, “Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki,” Science of the Total Environment, vol. 409, no. 8, pp. 1559–1571, 2011.
[28]
Y. Bai, L. Yong, W. Xiaoxue, X. Jingjing, and L. Chuan, “Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions,” Atmospheric Pollution Research, vol. 7, no. 3, pp. 557–566, 2016.
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Copyright © 2020 Bingchun Liu et al.
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Published: 01 January 2020
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