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Sensors 2016, 16(1), 86; doi:10.3390/s16010086

RAQ–A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems

1
Software College, Northeastern University, Shenyang 110819, China
2
Department of Computer Science, Rutgers University, New Brunswick, NJ 08854, USA
3
Department of Internet of Things Engineering, Hohai University, Changzhou 213022, China
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 30 September 2015 / Revised: 26 December 2015 / Accepted: 7 January 2016 / Published: 9 January 2016
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [5733 KB, uploaded 11 January 2016]   |  

Abstract

Air quality information such as the concentration of PM2.5 is of great significance for human health and city management. It affects the way of traveling, urban planning, government policies and so on. However, in major cities there is typically only a limited number of air quality monitoring stations. In the meantime, air quality varies in the urban areas and there can be large differences, even between closely neighboring regions. In this paper, a random forest approach for predicting air quality (RAQ) is proposed for urban sensing systems. The data generated by urban sensing includes meteorology data, road information, real-time traffic status and point of interest (POI) distribution. The random forest algorithm is exploited for data training and prediction. The performance of RAQ is evaluated with real city data. Compared with three other algorithms, this approach achieves better prediction precision. Exciting results are observed from the experiments that the air quality can be inferred with amazingly high accuracy from the data which are obtained from urban sensing. View Full-Text
Keywords: air quality prediction; random forest; point of interest; traffic air quality prediction; random forest; point of interest; traffic
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Yu, R.; Yang, Y.; Yang, L.; Han, G.; Move, O.A. RAQ–A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems. Sensors 2016, 16, 86.

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