Abstract
Aiming at the problem of missing values in air pollutant data and the single structure of prediction model, it is also need to consider that air pollutants will be affected by meteorological data and change quickly. Therefore, this paper mainly studies short-term (hourly) air quality index prediction. First, original pollutant concentration data are converted into individual air quality index of each pollutant item through calculation formula of AQI. Then, according to the missing data attributes and length of missing time, a combined missing processing method is proposed. After correlation analysis and feature selection, the air quality index prediction model using deep recurrent neural network based on gated recurrent unit (GRU-BPNN) is constructed to obtain the final predicted value, it is then classified to obtain its corresponding AQI level. Based on the real data sources obtained in Changchun, a large number of experiments have been carried out to prove that our model can improve the performance of air pollution prediction.
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Chen, Z., Zhang, Y., Liu, G., Guo, J. (2020). Air Quality Index Prediction Based on Deep Recurrent Neural Network. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12239. Springer, Cham. https://doi.org/10.1007/978-3-030-57884-8_26
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DOI: https://doi.org/10.1007/978-3-030-57884-8_26
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