Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning
Joint Authors
Yoon, Yourim
Kim, Yong-Hyuk
Kim, Na-Young
Im, Hyo-Hyuc
Choi, Reno K. Y.
Ha, Ji-Hun
Sim, Sangjin
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-07-25
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Kim, Yong-Hyuk& Ha, Ji-Hun& Yoon, Yourim& Kim, Na-Young& Im, Hyo-Hyuc& Sim, Sangjin…[et al.]. 2016. Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1099820
Modern Language Association (MLA)
Kim, Yong-Hyuk…[et al.]. Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-12.
https://search.emarefa.net/detail/BIM-1099820
American Medical Association (AMA)
Kim, Yong-Hyuk& Ha, Ji-Hun& Yoon, Yourim& Kim, Na-Young& Im, Hyo-Hyuc& Sim, Sangjin…[et al.]. Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-12.
https://search.emarefa.net/detail/BIM-1099820
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099820