气候变化领域集成服务门户(CCPortal): OzoneNet:A spatiotemporal information attention encoder model for ozone concentrations prediction with multi-source data

CCPortal

DOI	10.1007/s11869-024-01568-5
	OzoneNet:A spatiotemporal information attention encoder model for ozone concentrations prediction with multi-source data
	Tian, Wei; Ge, Zhongqi; He, Jianjun
发表日期	2024
ISSN	1873-9318
EISSN	1873-9326
英文摘要	Surface ozone (O3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O_3$$\end{document}) pollution is a serious environmental problem that endangers human health, and it is also an increasingly prominent environmental problem in the World. Existing works focus on how to directly improve the accuracy of predicting the target sequence from the input sequence while ignoring the inherent uncertainty of ozone in the atmosphere during the modeling process. Therefore, we utilize data fusion techniques to integrate ground observation data, satellite data, and reanalysis data for simulating atmospheric dynamics and enhancing prediction accuracy. We developed a sequence to sequence using a unit embedded with spatiotemporal information self attention mechanism as its encoder (OzoneNet) predict ozone concentration in the future. In the proposed method, we utilize the LSTM model with Spatiotemporal information self-attention mechanism to extract fixed Spatiotemporal data features, and the temporal dimension characteristics in long-term series are modeled by sequence-to-sequence network. Results show that the model has higher reliability and validity, outperforming benchmark models in simulating future changes in O3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$O_3$$\end{document} concentrations. The progeress of this method can help the public take corresponding protective measures, provide scientific guidance for the government's coordinated control of regional pollution, and can also provide important references for environmental protection and climate change research
英文关键词	Ozone concentration prediction; LSTM; Spatiotemporal information self-attention mechanism
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001220451700001
来源期刊	AIR QUALITY ATMOSPHERE AND HEALTH
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/301581
作者单位	Nanjing University of Information Science & Technology; China Meteorological Administration; Chinese Academy of Meteorological Sciences (CAMS); China Meteorological Administration; Chinese Academy of Meteorological Sciences (CAMS)
推荐引用方式 GB/T 7714	Tian, Wei,Ge, Zhongqi,He, Jianjun. OzoneNet:A spatiotemporal information attention encoder model for ozone concentrations prediction with multi-source data[J],2024.
APA	Tian, Wei,Ge, Zhongqi,&He, Jianjun.(2024).OzoneNet:A spatiotemporal information attention encoder model for ozone concentrations prediction with multi-source data.AIR QUALITY ATMOSPHERE AND HEALTH.
MLA	Tian, Wei,et al."OzoneNet:A spatiotemporal information attention encoder model for ozone concentrations prediction with multi-source data".AIR QUALITY ATMOSPHERE AND HEALTH (2024).