Abstract
With the development of quality of life, people pay more and more attention to the surrounding environmental factors, especially air pollution. The problem of air pollution in China is becoming more and more serious, which poses a great threat to people’s health. Therefore, the prediction of air quality concentration is very important. PM2.5 is the primary indicator for evaluating the concentration of smog. Currently, studies have been proposed to predict the concentration of PM2.5. Before, most methods use traditional machine learning or real-time monitoring to predict pollution of PM2.5 value. However, the previous prediction methods cannot meet the requirement of the accuracy. For this end, this paper uses Convnet and Dense-based Bidirectional Gated Recurrent Unit to predict PM2.5 value which combined Convnet, Dense and Bi-GRU. The feature in air quality data was extracted from convnets without max-pooling instead another convolutional layer and Bi-GRU with additional Dense could provide a more accuracy result. Experiments show that the effectiveness of our method PM2.5 mass concentration prediction model provides a more superior method for PM2.5 mass concentration prediction.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China under Grant 61972207, U1836208, U1836110, 61672290; the Major Program of the National Social Science Fund of China under Grant No. 17ZDA092, by the National Key R&D Program of China under grant 2018YFB1003205; by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) fund, China; by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund.
Funding
This work is supported by the National Natural Science Foundation of China under Grant 61972207, U1836208, U1836110, 61672290; the Major Program of the National Social Science Fund of China under Grant No. 17ZDA092, by the National Key R&D Program of China under grant 2018YFB1003205; by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) fund, China; by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund.
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Wang, B., Kong, W. & Zhao, P. An air quality forecasting model based on improved convnet and RNN. Soft Comput 25, 9209–9218 (2021). https://doi.org/10.1007/s00500-021-05843-w
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DOI: https://doi.org/10.1007/s00500-021-05843-w