Articles | Volume 13, issue 3
Geosci. Model Dev., 13, 1499–1511, 2020
Geosci. Model Dev., 13, 1499–1511, 2020

Model evaluation paper 25 Mar 2020

Model evaluation paper | 25 Mar 2020

PM2.5 ∕ PM10 ratio prediction based on a long short-term memory neural network in Wuhan, China

Xueling Wu et al.

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Cited articles

Arvani, B., Pierce, R. B., Lyapustin, A. I., Wang, Y., and Teggi, S.: Seasonal monitoring and estimation of regional aerosol distribution over Po valley, northern Italy, using a high-resolution MAIAC product, Atmos. Environ., 141, 106–121, 2016. 
Bao, G. and Zeng, Z.: Multistability of periodic delayed recurrent neural network with memristors, Neural Comput. Appl., 23, 1963–1967, 2013. 
Cai, H., Gui, K., and Chen, Q.: Changes in haze trends in the Sichuan-Chongqing region, China, 1980 to 2016, Atmosphere, 9, 277,, 2018. 
Chen, Q. X., Yuan, Y., Huang, X., Jiang, Y. Q., and Tan, H. P.: Estimation of surface-level PM2.5, concentration using aerosol optical thickness through aerosol type analysis method, Atmos. Environ., 159, 26–33, 2017. 
Chen, Z. Y., Zhang, T. H., Zhang, R., Zhu, Z. M., Ou, C. Q., and Guo, Y.: Estimating PM2.5 concentrations based on non-linear exposure-lag-response associations with aerosol optical depth and meteorological measures, Atmos. Environ., 173, 30–37, 2018. 
Short summary
This paper presents a composite prediction system designed to improve the accuracy and applicability of PM2.5 / PM10 predictions. Based on remote sensing images, the aerosol optical thickness was obtained and corrected. Then, we selected PM2.5 / PM10-related factors from meteorological factors and air pollutants and compared the effects of several intelligent models in different prediction patterns. The results showed that the LSTM model had significant advantages in accuracy and stability.