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A Long Short-Term Memory Neural Network for Daily NO2 Concentration Forecasting

A Long Short-Term Memory Neural Network for Daily NO2 Concentration Forecasting

Bingchun Liu, Xiaogang Yu, Qingshan Wang, Shijie Zhao, Lei Zhang
Copyright: © 2021 |Volume: 16 |Issue: 4 |Pages: 17
ISSN: 1554-1045|EISSN: 1554-1053|EISBN13: 9781799859772|DOI: 10.4018/IJITWE.2021100102
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MLA

Liu, Bingchun, et al. "A Long Short-Term Memory Neural Network for Daily NO2 Concentration Forecasting." IJITWE vol.16, no.4 2021: pp.35-51. http://doi.org/10.4018/IJITWE.2021100102

APA

Liu, B., Yu, X., Wang, Q., Zhao, S., & Zhang, L. (2021). A Long Short-Term Memory Neural Network for Daily NO2 Concentration Forecasting. International Journal of Information Technology and Web Engineering (IJITWE), 16(4), 35-51. http://doi.org/10.4018/IJITWE.2021100102

Chicago

Liu, Bingchun, et al. "A Long Short-Term Memory Neural Network for Daily NO2 Concentration Forecasting," International Journal of Information Technology and Web Engineering (IJITWE) 16, no.4: 35-51. http://doi.org/10.4018/IJITWE.2021100102

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Abstract

NO2 pollution has caused serious impact on people's production and life, and the management task is very difficult. Accurate prediction of NO2 concentration is of great significance for air pollution management. In this paper, a NO2 concentration prediction model based on long short-term memory neural network (LSTM) is constructed with daily NO2 concentration in Beijing as the prediction target and atmospheric pollutants and meteorological factors as the input indicators. Firstly, the parameters and architecture of the model are adjusted to obtain the optimal prediction model. Secondly, three different sets of input indicators are built on the basis of the optimal prediction model to enter the model learning. Finally, the impact of different input indicators on the accuracy of the model is judged. The results show that the LSTM model has high application value in NO2 concentration prediction. The maximum temperature and O3 among the three input indicators improve the prediction accuracy while the NO2 historical low-frequency data reduce the prediction accuracy.

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