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000893368 1001_ $$0P:(DE-Juel1)171435$$aBetancourt, Clara$$b0
000893368 245__ $$aAQ-Bench: a benchmark dataset for machine learning on global air quality metrics
000893368 260__ $$aKatlenburg-Lindau$$bCopernics Publications$$c2021
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000893368 520__ $$aWith the AQ-Bench dataset, we contribute to the recent developments towards shared data usage and machine learning methods in the field of environmental science. The dataset presented here enables researchers to relate global air quality metrics to easy-access metadata and to explore different machine learning methods for obtaining estimates of air quality based on this metadata. AQ-Bench contains a unique collection of aggregated air quality data from the years 2010–2014 and metadata at more than 5500 air quality monitoring stations all over the world, provided by the first Tropospheric Ozone Assessment Report (TOAR). It focuses in particular on metrics of tropospheric ozone, which has a detrimental effect on climate, human morbidity and mortality, as well as crop yields. The purpose of this dataset is to produce estimates of various long-term ozone metrics based on time-independent local site conditions. We combine this task with a suitable evaluation metric. Baseline scores obtained from a linear regression method, a fully connected neural network and random forest are provided for reference and validation. AQ-Bench offers a low-threshold entrance for all machine learners with an interest in environmental science and for atmospheric scientists who are interested in applying machine learning techniques. It enables them to start with a real-world problem relevant to humans and nature. The dataset and introductory machine learning code are available at https://doi.org/10.23728/b2share.30d42b5a87344e82855a486bf2123e9f (Betancourt et al., 2020) and https://gitlab.version.fz-juelich.de/esde/machine-learning/aq-bench (Betancourt et al., 2021). AQ-Bench thus provides a blueprint for environmental benchmark datasets as well as an example for data re-use according to the FAIR principles.
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000893368 536__ $$0G:(EU-Grant)787576$$aIntelliAQ - Artificial Intelligence for Air Quality (787576)$$c787576$$fERC-2017-ADG$$x1
000893368 536__ $$0G:(DE-Juel1)deepacf_20191101$$aDeep Learning for Air Quality and Climate Forecasts (deepacf_20191101)$$cdeepacf_20191101$$fDeep Learning for Air Quality and Climate Forecasts$$x2
000893368 536__ $$0G:(DE-Juel-1)ESDE$$aEarth System Data Exploration (ESDE)$$cESDE$$x3
000893368 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000893368 7001_ $$0P:(DE-HGF)0$$aStomberg, Timo$$b1
000893368 7001_ $$0P:(DE-HGF)0$$aRoscher, Ribana$$b2
000893368 7001_ $$0P:(DE-Juel1)6952$$aSchultz, Martin G.$$b3$$eCorresponding author
000893368 7001_ $$0P:(DE-Juel1)180752$$aStadtler, Scarlet$$b4$$ufzj
000893368 770__ $$aBenchmark datasets and machine learning algorithms for Earth system science data (ESSD/GMD inter-journal SI)
000893368 773__ $$0PERI:(DE-600)2475469-9$$a10.5194/essd-13-3013-2021$$gVol. 13, no. 6, p. 3013 - 3033$$n6$$p3013 - 3033$$tEarth system science data$$v13$$x1866-3516$$y2021
000893368 8564_ $$uhttps://juser.fz-juelich.de/record/893368/files/AQ-Bench%20paper.pdf$$yOpenAccess
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