Paper Info
Title
Improved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine Learning.
Abstract
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.
Year
DOI
Venue
2016
10.1155/2016/9467878
Comp. Int. and Neurosc.
Field
DocType
Volume
Time domain,Automatic weather station,Pattern recognition,Expectation–maximization algorithm,Regression analysis,Computer science,Support vector machine,Multilayer perceptron,Artificial intelligence,Cluster analysis,Machine learning,Linear regression
Journal
2016
ISSN
Citations 
PageRank 
1687-5265
0
0.34
References 
Authors
9
7
Name
Order
Citations
PageRank
Yong-Hyuk Kim135540.27
Jihun Ham200.34
Yourim Yoon318517.18
Na Young Kim421.74
Hyo-Hyuc Im520.72
Sangjin Sim600.34
Reno K. Y. Choi720.72