Abstract
In recent years, the rapid development of industrial technology has been accompanied by serious environmental pollution. In the face of numerous environmental pollution problems, particulate matter (PM2.5) which has received special attention is rich in a large amount of toxic and harmful substances. Furthermore, PM2.5 has a long residence time in the atmosphere and a long transport distance, so analyzing PM2.5 distributions is an important issue for air quality prediction. Therefore, this paper proposes a method based on convolutional neural networks (CNN) and long short-term memory (LSTM) networks to analyze the spatial-temporal characteristics of PM2.5 distributions for predicting air quality in multiple cities. In experiments, the records of environmental factors in China were collected and analyzed, and three accuracy metrics (i.e., mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE)) were used to evaluate the performance of the proposed method in this paper. For the evaluation of the proposed method, the performance of the proposed method was compared with other machine learning methods. The practical experimental results show that the MAE, RMSE, and MAPE of the proposed method are lower than other machine learning methods. The main contribution of this paper is to propose a deep multilayer neural network that combines the advantages of CNN and LSTM for accurately predicting air quality in multiple cities.
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Acknowledgment
This work was supported in part the National Natural Science Foundation of China under Grants No. 61877010 and No. 11501114, and the Fujian Natural Science Funds under Grant No. 2019J01243. This research was partially supported by Fuzhou University, grant numbers 510730/XRC-18075 and 510809/GXRC-19037.
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Guo, C., Guo, W., Chen, CH., Wang, X., Liu, G. (2019). The Air Quality Prediction Based on a Convolutional LSTM Network. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_12
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DOI: https://doi.org/10.1007/978-3-030-30952-7_12
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