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Computational Intelligence and Neuroscience
Volume 2016, Article ID 9467878, 12 pages
Research Article

Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning

1Department of Computer Science and Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01890, Republic of Korea
2Department of Embedded Software Engineering, Kwangwoon University, 20 Kwangwoon-ro, Nowon-gu, Seoul 01890, Republic of Korea
3Department of Computer Engineering, College of Information Technology, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do 13120, Republic of Korea
4Korea Oceanic and Atmospheric System Technology, No. 1503, STX W-Tower, 90, Gyeongin-ro 53-gil, Guro-gu, Seoul 08215, Republic of Korea
5Observation Research Division, National Institute of Meteorological Sciences, 33 Seohobuk-ro, Seogwipo-gi, Jeju-do 63568, Republic of Korea
6Geography and Environment, University of Southampton, University Road, Southampton SO17 1BJ, UK

Received 6 November 2015; Accepted 9 June 2016

Academic Editor: Elio Masciari

Copyright © 2016 Yong-Hyuk Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A correction method using machine learning aims to improve the conventional linear regression (LR) based method for correction of atmospheric pressure data obtained by smartphones. The method proposed in this study conducts clustering and regression analysis with time domain classification. Data obtained in Gyeonggi-do, one of the most populous provinces in South Korea surrounding Seoul with the size of 10,000 km2, from July 2014 through December 2014, using smartphones were classified with respect to time of day (daytime or nighttime) as well as day of the week (weekday or weekend) and the user’s mobility, prior to the expectation-maximization (EM) clustering. Subsequently, the results were analyzed for comparison by applying machine learning methods such as multilayer perceptron (MLP) and support vector regression (SVR). The results showed a mean absolute error (MAE) 26% lower on average when regression analysis was performed through EM clustering compared to that obtained without EM clustering. For machine learning methods, the MAE for SVR was around 31% lower for LR and about 19% lower for MLP. It is concluded that pressure data from smartphones are as good as the ones from national automatic weather station (AWS) network.