globalchange  > 气候变化与战略
DOI: 10.1016/j.atmosenv.2020.117535
论文题名:
OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning
作者: Lautenschlager F.; Becker M.; Kobs K.; Steininger M.; Davidson P.; Krause A.; Hotho A.
刊名: Atmospheric Environment
ISSN: 1352-2310
出版年: 2020
卷: 233
语种: 英语
英文关键词: 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
学科: Automated machine learning ; Land use regression ; LUR ; OpenStreetMap ; Pollution
中文摘要: 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
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/160296
Appears in Collections:气候变化与战略

Files in This Item:

There are no files associated with this item.


作者单位: Chair for Data Science, Computer Science, University of Würzburg, Am Hubland, Würzburg, 97074, Germany; Stanford University, United States

Recommended Citation:
Lautenschlager F.,Becker M.,Kobs K.,et al. OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning[J]. Atmospheric Environment,2020-01-01,233
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Lautenschlager F.]'s Articles
[Becker M.]'s Articles
[Kobs K.]'s Articles
百度学术
Similar articles in Baidu Scholar
[Lautenschlager F.]'s Articles
[Becker M.]'s Articles
[Kobs K.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Lautenschlager F.]‘s Articles
[Becker M.]‘s Articles
[Kobs K.]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.