气候变化领域集成服务门户(CCPortal): OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning

CCPortal

DOI	10.1016/j.atmosenv.2020.117535
	OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning
	Lautenschlager F.; Becker M.; Kobs K.; Steininger M.; Davidson P.; Krause A.; Hotho A.
发表日期	2020
ISSN	1352-2310
卷号	233
英文摘要	To assess the exposure of citizens to pollutants like NOx or particulate matter in urban areas, land use regression (LUR) models are a well established method. LUR models leverage information about environmental and anthropogenic factors such as cars, heating, or industry to predict air pollution in areas where no measurements have been made. However, existing approaches are often not globally applicable and require tedious hyper-parameter tuning to enable high quality predictions. In this work, we tackle these issues by introducing OpenLUR, an off-the-shelf approach for modeling air pollution that (i) works on a set of novel features solely extracted from the globally and openly available data source OpenStreetMap and (ii) is based on state-of-the-art machine learning featuring automated hyper-parameter tuning in order to minimize manual effort. We show that our proposed features are able to outperform their counterparts from local and closed sources, and illustrate how automated hyper parameter tuning can yield competitve results while alleviating the need for expert knowledge in machine learning and manual effort. Importantly, we further demonstrate the potential of the global availability of our features by applying cross-learning across different cities in order to reduce the need for a large amount of training samples. Overall, OpenLUR represents an off-the-shelf approach that facilitates easily reproducible experiments and the development of globally applicable models. © 2020 Elsevier Ltd
关键词	Automated machine learning Land use regression LUR OpenStreetMap Pollution
语种	英语
scopus关键词	Air pollution; Land use; Air Pollution Modeling; Anthropogenic factors; Closed source; Expert knowledge; Hyper-parameter; Land use regression; Particulate Matter; Training sample; Machine learning; anthropogenic source; atmospheric pollution; machine learning; numerical model; particulate matter; regression analysis; software; air pollution; article; city; land use; machine learning
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/249123
作者单位	Chair for Data Science, Computer Science, University of Würzburg, Am Hubland, Würzburg, 97074, Germany; Stanford University, United States
推荐引用方式 GB/T 7714	Lautenschlager F.,Becker M.,Kobs K.,et al. OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning[J],2020,233.
APA	Lautenschlager F..,Becker M..,Kobs K..,Steininger M..,Davidson P..,...&Hotho A..(2020).OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning.ATMOSPHERIC ENVIRONMENT,233.
MLA	Lautenschlager F.,et al."OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning".ATMOSPHERIC ENVIRONMENT 233(2020).