深度学习方法在上海市PM<sub>2.5</sub>浓度预报中的应用

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Abstract

In order to improve the forecasting ability of PM_2.5 concentration, especially the forecasting ability of PM_2.5 heavy pollution, this study was based on the mesoscale meteorological-chemical coupled modeling system (WRF-Chem) forecast data in combination with weather and PM_2.5 (fine particulate matter) observational data, a machine learning model of PM_2.5 prediction in Shanghai, China was established. The results showed that the machine learning algorithm combined with WRF-Chem prediction obviously corrected the deviation of model prediction due to the non-objectivity of the model, and improved the prediction effect. The linear regression method (Lasso) could not obtain a suitable optimization effect, and the deep learning sequence to sequence algorithm was selected to improve the model and verified by experiments. The overall correlation coefficient of the method for the PM_2.5 prediction increased from 0.51 to 0.79. The root mean square error was reduced from 25.9μg/m³ to 15.0μg/m³. The Seq2seq modified PM_2.5 forecasting model based on WRF-Chem can effectively improve the prediction accuracy. It took a useful application prospect in air quality forecast.

Key words： sequence to sequence model PM_2.5 forecast WRF-Chem Shanghai

Received: 23 July 2019

PACS:

X513

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	Articles by authors
	MA Jing-hui
	CAO Yu
	YU Zhong-qi
	QU Yuan-hao
	XU Jian-ming

Cite this article:

MA Jing-hui,CAO Yu,YU Zhong-qi等. The application of deep learning method in Shanghai PM_2.5 prediction[J]. CHINA ENVIRONMENTAL SCIENCECE, 2020, 40(2): 530-538.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2020/V40/I2/530

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