干旱区生态环境知识资源中心(Arid): Air Quality Forecast through Integrated Data Assimilation and Machine Learning

Arid

DOI	10.5220/0007555207870793
	Air Quality Forecast through Integrated Data Assimilation and Machine Learning
	Lin, Hai Xiang; Jin, Jianbing; van den Herik, Jaap
通讯作者	Lin, HX (corresponding author), Delft Univ Technol, Delft Inst Appl Math, Delft, Netherlands. ; Lin, HX (corresponding author), Leiden Univ, Leiden, Netherlands.
会议名称	11th International Conference on Agents and Artificial Intelligence (ICAART)
会议日期	FEB 19-21, 2019
会议地点	Prague, CZECH REPUBLIC
英文摘要	Numerical models of chemical transport have been used to simulate the complex processes involved in the formation and transport of air pollutants. Although these models can predict the spatiotemporal variability of a variety of chemical species, the accuracy of these models is often limited. Therefore, in the past two decades, data assimilation methods have been applied to use the available measurements for improving the forecast. Nowadays, machine learning techniques provide new opportunities for improving the air quality forecast. A case study on PM10 concentrations during a dust storm is performed. It is known that the PM10 concentrations are caused by multiple emission sources, e.g., dust from desert and anthropogenic emissions. An accurate modeling of the PM10 concentration levels owing to the local anthropogenic emissions is essential for an adequate evaluation of the dust level. However, real-time measurement of local emissions is not possible, so no direct data is available. Actually, the lack of in-time emission inventories is one of the main reasons that current numerical chemical transport models cannot produce accurate anthropogenic PM10 simulations. Using machine learning techniques to generate local emissions based on real-time observations is a promising approach. We report how it can be combined with data assimilation to improve the accuracy of air quality forecast considerably.
英文关键词	Chemical Transport Model Data-driven Machine Learning Physics-based Machine Learning
来源出版物	PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE (ICAART), VOL 2
出版年	2019
页码	787-793
ISBN	978-989-758-350-6
出版者	SCITEPRESS
类型	Proceedings Paper
语种	英语
开放获取类型	hybrid
收录类别	CPCI-S
WOS记录号	WOS:000671841000086
WOS关键词	PARADIGM ; CHINA ; PM2.5
WOS类目	Computer Science, Artificial Intelligence
WOS研究方向	Computer Science
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/370236
作者单位	[Lin, Hai Xiang; Jin, Jianbing] Delft Univ Technol, Delft Inst Appl Math, Delft, Netherlands; [Lin, Hai Xiang; van den Herik, Jaap] Leiden Univ, Leiden, Netherlands
推荐引用方式 GB/T 7714	Lin, Hai Xiang,Jin, Jianbing,van den Herik, Jaap. Air Quality Forecast through Integrated Data Assimilation and Machine Learning[C]:SCITEPRESS,2019:787-793.