| Issue |
MATEC Web Conf.
Volume 103, 2017
International Symposium on Civil and Environmental Engineering 2016 (ISCEE 2016)
|
|
|---|---|---|
| Article Number | 05001 | |
| Number of page(s) | 8 | |
| Section | Sustainable Environmental Sciences and Technology | |
| DOI | https://doi.org/10.1051/matecconf/201710305001 | |
| Published online | 05 April 2017 | |
Characteristic and Prediction of Carbon Monoxide Concentration using Time Series Analysis in Selected Urban Area in Malaysia
1 Faculty of Civil and Enviromental Engineering, Universiti Tun Hussein Onn Malaysia
2 Faculty of Computer Sciences and Mathematics, Universiti Teknologi MARA
* Corresponding author: hazrul@uthm.edu.my
Carbon monoxide (CO) is a poisonous, colorless, odourless and tasteless gas. The main source of carbon monoxide is from motor vehicles and carbon monoxide levels in residential areas closely reflect the traffic density. Prediction of carbon monoxide is important to give an early warning to sufferer of respiratory problems and also can help the related authorities to be more prepared to prevent and take suitable action to overcome the problem. This research was carried out using secondary data from Department of Environment Malaysia from 2013 to 2014. The main objectives of this research is to understand the characteristic of CO concentration and also to find the most suitable time series model to predict the CO concentration in Bachang, Melaka and Kuala Terengganu. Based on the lowest AIC value and several error measure, the results show that ARMA (1,1) is the most appropriate model to predict CO concentration level in Bachang, Melaka while ARMA (1,2) is the most suitable model with smallest error to predict the CO concentration level for residential area in Kuala Terengganu.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.