Abstract
The Air Quality Index (AQI) is a significant indicator that can intuitively reflect the levels of air pollution. Accurate forecasting of AQI will help governments control air pollution problems and prevent citizens from a smoggy environment. In this research, we propose a general hybrid model for short-term AQI forecasting. First, we adopt the Empirical Mode Decomposition (EMD) method to decompose historical AQI time series for extracting decomposed components as features. Then, the decomposed components of AQI and the concentration of other air pollutants, such as PM2.5, PM10 and etc., are utilized as input features to train 2 parallel 1D Convolutional neural networks (1DCNN). Finally, the output of the 1DCNN is adopted as input features for train a Long short-term memory (LSTM) network. Experimental based on datasets from 2 observation stations demonstrated that the proposed hybrid model performs the best results.
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Acknowledgments
This work was supported by Natural Science Foundation of Guangdong Province, China (2020A1515010761), Science and Technology Program of Guangzhou, China (201904010224), and National Science Foundation of China Project 72004174.
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Jiang, W., Fu, Y., Lin, F., Liu, J., Zhan, C. (2021). Empirical Mode Decomposition Based Deep Neural Networks for AQI Forecasting. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds) Neural Computing for Advanced Applications. NCAA 2021. Communications in Computer and Information Science, vol 1449. Springer, Singapore. https://doi.org/10.1007/978-981-16-5188-5_54
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DOI: https://doi.org/10.1007/978-981-16-5188-5_54
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