• Jan 05, 2017 News![CFP] 2017 the annual meeting of IJFCC Editorial Board, ICCTD 2017, will be held in Paris, France during March 20-22, 2017.   [Click]
  • Mar 24, 2016 News! IJFCC Vol. 4, No. 4 has been indexed by EI (Inspec).   [Click]
  • Jun 28, 2017 News!Vol.6, No.3 has been published with online version.   [Click]
General Information
    • ISSN: 2010-3751
    • Frequency: Bimonthly (2012-2016); Quarterly (Since 2017)
    • DOI: 10.18178/IJFCC
    • Editor-in-Chief: Prof. Mohamed Othman
    • Executive Editor: Ms. Nancy Y. Liu
    • Abstracting/ Indexing: Google Scholar, Engineering & Technology Digital Library, and Crossref, DOAJ, Electronic Journals LibraryEI (INSPEC, IET).
    • E-mail:  ijfcc@ejournal.net 
Editor-in-chief
Prof. Mohamed Othman
Department of Communication Technology and Network Universiti Putra Malaysia, Malaysia
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.
IJFCC 2017 Vol.6(3): 128-132 ISSN: 2010-3751
doi: 10.18178/ijfcc.2017.6.3.504

The Forecast of PM10 Pollutant by Using a Hybrid Model

Ronnachai Chuentawat, Nittaya Kerdprasop, and Kittisak Kerdprasop
Abstract—This research aims to study the forecasting model to predict the 24-hour average PM10 concentration in the Northern region of Thailand. This research presents a hybrid model that combines the autoregressive part of the Autoregressive Integrated Moving Average (ARIMA) model with the support vector regression technique. The data used in this study are the 24-hour average PM10 concentration from 3 locations. Each of the data sets is the daily univariate time series during 1st January to 31th May 2016. We evaluate predictive performance of our hybrid model using the two measurements: Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The performance of our hybrid model has been compared against the ARIMA model. From the experimental results, we found that a hybrid model has lower RMSE and MAPE than the ARIMA model for all three data sets. Therefore, we concluded that our hybrid model can be used to forecast the 24-hour average PM10 concentration in the Northern region of Thailand.

Index Terms—PM10, ARIMA model, support vector regression, hybrid model.

Ronnachai Chuentawat is with the Nakhonratchasima Rajabhat University, Thailand (e-mail: c_ronnachai@hotmail.com).
Nittaya Kerdprasop and Kittisak Kerdprasop are with the Suranaree University of Technology, Thailand (e-mail: nittaya@sut.ac.th, kerdpras@sut.ac.th).

[PDF]

Cite: Ronnachai Chuentawat, Nittaya Kerdprasop, and Kittisak Kerdprasop, "The Forecast of PM10 Pollutant by Using a Hybrid Model," International Journal of Future Computer and Communication vol. 6, no. 3, pp. 128-132, 2017.

Copyright © 2008-2016. International Journal of Future Computer and Communication. All rights reserved.
E-mail: ijfcc@ejournal.net