气候变化领域集成服务门户(CCPortal): A novel framework for daily forecasting of ozone mass concentrations based on cycle reservoir with regular jumps neural networks

CCPortal

DOI	10.1016/j.atmosenv.2019.117072
	A novel framework for daily forecasting of ozone mass concentrations based on cycle reservoir with regular jumps neural networks
	Mo Y.; Li Q.; Karimian H.; Fang S.; Tang B.; Chen G.; Sachdeva S.
发表日期	2020
ISSN	13522310
卷号	220
英文摘要	An understanding of the growth in surface concentration of ozone and its adverse health effects are important for environmental departments to make sensible decisions for future. Our hybrid model CEEMD+CRJ+MLR is the first attempt to improve CRJ in the field of air pollution prediction and ozone forecasting. For this novel framework, CEEMD has been adopted to decompose original MDA8_O3 history into several sub-series. After that, for each IMF, CRJ is used to extract time-series features. These time-series features are fed into appropriate machine learning methods for prediction. In addition to that, residual is also predicted through normal methods. A model, which is trained with data from 1 May 2014 to 31 May 2017, is validated with data from 1 June 2017 to 30 May 2018, obtained from four stations of Beijing, China. The hybrid model has input variables which are combined with related pollutants, meteorological forecasts and UV index, and predict maximum daily 8-h average ozone (MDA8_O3) concentration in different time intervals. Our experimental results show that the CEEMD+CRJ+MLR model exhibits the best performance compared with other benchmark models generally. For four stations, IA, MAE, RMSE and MAPE average of +1 (forecasting 1 day in advance) are 0.9763, 12.84, 17.81 and 18.5% respectively and of +2 are 0.9679, 15.17, 20.15 and 23.86% respectively. Especially in the case of forecasting heavy ozone concentration (Level III), a critical issue in air pollution predictions, the classification rate of our hybrid model has improved from 29.4% (for CRJ) to 83.4% in +1 and from 38% (for CRJ) to 73% in +2. For long time forecasting, the CEEMD+CRJ+MLR also shows its outstanding performance in whole levels and level III ozone concentration. Our hybrid model, with accurate and stable results, is highly effective for MDA8_O3 concentration prediction and can efficiently be applied in other regions. © 2019 Elsevier Ltd
英文关键词	Air pollution; Deep neural networks; Machine learning; Ozone; Prediction; Recurrent neural networks
学科领域	Air pollution; Benchmarking; Deep neural networks; Learning systems; Machine learning; Ozone; Recurrent neural networks; Time series; Adverse health effects; Air pollution predictions; Classification rates; Concentration prediction; Machine learning methods; Ozone concentration; Surface concentration; Time series features; Forecasting; ozone; artificial neural network; atmospheric pollution; concentration (composition); machine learning; ozone; prediction; air monitoring; air pollution; air quality standard; Article; China; decomposition; forecasting; intrinsic mode function; machine learning; priority journal; recurrent neural network; support vector machine; time series analysis; Beijing [China]; China
语种	英语
scopus关键词	Air pollution; Benchmarking; Deep neural networks; Learning systems; Machine learning; Ozone; Recurrent neural networks; Time series; Adverse health effects; Air pollution predictions; Classification rates; Concentration prediction; Machine learning methods; Ozone concentration; Surface concentration; Time series features; Forecasting; ozone; artificial neural network; atmospheric pollution; concentration (composition); machine learning; ozone; prediction; air monitoring; air pollution; air quality standard; Article; China; decomposition; forecasting; intrinsic mode function; machine learning; priority journal; recurrent neural network; support vector machine; time series analysis; Beijing [China]; China
来源期刊	Atmospheric Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/120885
作者单位	Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing, China; School of Architecture, Surveying and Mapping Engineering, Jiangxi University of Science and TechnologyJiangxi, China; Shenzhen Engineering Laboratory of Ocean Environmental Big Data Analysis and Application, Shenzhen, 518055, China
推荐引用方式 GB/T 7714	Mo Y.,Li Q.,Karimian H.,et al. A novel framework for daily forecasting of ozone mass concentrations based on cycle reservoir with regular jumps neural networks[J],2020,220.
APA	Mo Y..,Li Q..,Karimian H..,Fang S..,Tang B..,...&Sachdeva S..(2020).A novel framework for daily forecasting of ozone mass concentrations based on cycle reservoir with regular jumps neural networks.Atmospheric Environment,220.
MLA	Mo Y.,et al."A novel framework for daily forecasting of ozone mass concentrations based on cycle reservoir with regular jumps neural networks".Atmospheric Environment 220(2020).