Prediction of Aerosol Extinction Coefficient in Coastal Areas of South China Based on Attention-BiLSTM

doi:10.3390/jmse10040545

	Prediction of Aerosol Extinction Coefficient in Coastal Areas of South China Based on Attention-BiLSTM
	Ye, Zhou 1,2,3; Cui, Shengcheng 1,2; Qiao, Zhi 1,2,3; Zhang, Zihan 1,2; Zhu, Wenyue 1,2; Li, Xuebin 1,2; Qian, Xianmei 1,2
刊名	JOURNAL OF MARINE SCIENCE AND ENGINEERING
	2022-04-01
卷号	10
关键词	attention mechanism long short-term memory aerosol extinction coefficient prediction
DOI	10.3390/jmse10040545
通讯作者	Cui, Shengcheng(csc@aiofm.ac.cn)
英文摘要	The aerosol extinction coefficient (AEC) characterises the attenuation of the light propagating in a turbid medium with suspended particles. Therefore, it is of great significance to carry out AEC prediction research using state-of-art neural network (NN) methods. The attention mechanism (AM) has become an indispensable part of NNs that focuses on input weight assignment. Traditional AM is used in time steps to help generate the outputs. To select important features of meteorological parameters (MP) that are helpful for forecasting, in this study, we apply AM to features instead of time steps. Then we propose a bidirectional long short-term memory (BiLSTM) NN based on AM to predict the AEC. The proposed method can remember information twice (i.e., forward and backward), which can provide more context for AEC forecasting. Finally, an in situ measured MP dataset is applied in the proposed model, which presents Maoming coastal area's atmospheric conditions in November 2020. The experimental results show that the model proposed in this paper has higher accuracy compared with traditional NN, providing a novel solution to the AEC prediction problem for the current studies of marine aerosol.
资助项目	Foundation of Key Laboratory of Science and Technology Innovation of Chinese Academy of Sciences[CXJJ-21S028] ; Thirteenth Five-Year Equipment Pre-Research Sharing Technology Project[41416030204] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA17010104] ; Youth spark project of Hefei Institute of material sciences, Chinese Academy of Sciences[29YZJJ2020QN2]
WOS关键词	RECURRENT NEURAL-NETWORKS ; MARINE AEROSOL ; CLIMATE ; TRANSPORT
WOS研究方向	Engineering ; Oceanography
语种	英语
出版者	MDPI
WOS记录号	WOS:000785423200001
资助机构	Foundation of Key Laboratory of Science and Technology Innovation of Chinese Academy of Sciences ; Thirteenth Five-Year Equipment Pre-Research Sharing Technology Project ; Strategic Priority Research Program of Chinese Academy of Sciences ; Youth spark project of Hefei Institute of material sciences, Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.hfcas.ac.cn:8080/handle/334002/128501]
专题	中国科学院合肥物质科学研究院
通讯作者	Cui, Shengcheng
作者单位	1.Chinese Acad Sci, Anhui Inst Opt & Fine Mech, Key Lab Atmospher Opt, Hefei 230031, Peoples R China 2.Adv Laser Technol Lab Anhui Prov, Hefei 230037, Peoples R China 3.Univ Sci & Technol China, Sci Isl Branch Grad Sch, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Ye, Zhou,Cui, Shengcheng,Qiao, Zhi,et al. Prediction of Aerosol Extinction Coefficient in Coastal Areas of South China Based on Attention-BiLSTM[J]. JOURNAL OF MARINE SCIENCE AND ENGINEERING,2022,10.
APA	Ye, Zhou.,Cui, Shengcheng.,Qiao, Zhi.,Zhang, Zihan.,Zhu, Wenyue.,...&Qian, Xianmei.(2022).Prediction of Aerosol Extinction Coefficient in Coastal Areas of South China Based on Attention-BiLSTM.JOURNAL OF MARINE SCIENCE AND ENGINEERING,10.
MLA	Ye, Zhou,et al."Prediction of Aerosol Extinction Coefficient in Coastal Areas of South China Based on Attention-BiLSTM".JOURNAL OF MARINE SCIENCE AND ENGINEERING 10(2022).