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GI_Forum 2018, Volume 6, Issue 1Journal for Geographic Information Science
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2018, Volume 6, Issue 1, pp. 65-81, 2018/06/22
Journal for Geographic Information Science
Today, we have access to a vast amount of weather, air quality, noise or radioactivity data collected by individuals around the globe. This volunteered geographic information often contains data of uncertain and of heterogeneous quality, in particular when compared to official in-situ measurements. This limits their application, as rigorous, work-intensive data-cleaning has to be performed, which reduces the amount of data and cannot be performed in real-time. In this paper, we propose a method to evaluate dynamically learning the quality of individual sensors by optimizing a weighted Gaussian process regression using an evolutionary algorithm. The evaluation was carried out in south-west Germany in August 2016 for temperature data from the Wunderground network and the Deutsche Wetter Dienst (DWD), in total 1,561 stations. Using a 10-fold cross-validation scheme based on the DWD ground truth, we show significant improvements for the predicted sensor readings: we obtained a 12.5% improvement on the mean absolute error.
Keywords: crowdsourcing air temperature; data quality assessment; Evolutionary Learning; Gaussian process regression; volunteered geographic information.