气候变化领域集成服务门户(CCPortal): Development of a daily PM10 and PM2.5 prediction system using a deep long short-term memory neural network model

CCPortal

DOI	10.5194/acp-19-12935-2019
	Development of a daily PM10 and PM2.5 prediction system using a deep long short-term memory neural network model
	Kim H.S.; Park I.; Song C.H.; Lee K.; Yun J.W.; Kim H.K.; Jeon M.; Lee J.; Han K.M.
发表日期	2019
ISSN	16807316
起始页码	12935
结束页码	12951
卷号	19 期号:20
英文摘要	A deep recurrent neural network system based on a long short-term memory (LSTM) model was developed for daily PM10 and PM2.5 predictions in South Korea. The structural and learnable parameters of the newly developed system were optimized from iterative model training. Independent variables were obtained from ground-based observations over 2.3 years. The performance of the particulate matter (PM) prediction LSTM was then evaluated by comparisons with ground PM observations and with the PM concentrations predicted from two sets of 3-D chemistry-transport model (CTM) simulations (with and without data assimilation for initial conditions). The comparisons showed, in general, better performance with the LSTM than with the 3-D CTM simulations. For example, in terms of IOAs (index of agreements), the PM prediction IOAs were enhanced from 0.36-0.78 with the 3-D CTM simulations to 0.62-0.79 with the LSTM-based model. The deep LSTM-based PM prediction system developed at observation sites is expected to be further integrated with 3-D CTM-based prediction systems in the future. In addition to this, further possible applications of the deep LSTM-based system are discussed, together with some limitations of the current system. © 2019 SPIE. All rights reserved.
语种	英语
scopus关键词	artificial neural network; concentration (composition); particulate matter; prediction; South Korea
来源期刊	Atmospheric Chemistry and Physics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/144086
作者单位	School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, South Korea; School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, South Korea
推荐引用方式 GB/T 7714	Kim H.S.,Park I.,Song C.H.,et al. Development of a daily PM10 and PM2.5 prediction system using a deep long short-term memory neural network model[J],2019,19(20).
APA	Kim H.S..,Park I..,Song C.H..,Lee K..,Yun J.W..,...&Han K.M..(2019).Development of a daily PM10 and PM2.5 prediction system using a deep long short-term memory neural network model.Atmospheric Chemistry and Physics,19(20).
MLA	Kim H.S.,et al."Development of a daily PM10 and PM2.5 prediction system using a deep long short-term memory neural network model".Atmospheric Chemistry and Physics 19.20(2019).