Sains Malaysiana 45(11)(2016): 1625–1633

 

Evaluation Performance of Time Series Approach for Forecasting Air Pollution Index in Johor

(Penilaian Prestasi Pendekatan Siri Masa untuk Peramalan Indeks Pencemaran Udara di Johor)

 

NUR HAIZUM ABD RAHMAN1, MUHAMMAD HISYAM LEE1*, SUHARTONO2

& MOHD TALIB LATIF3

 

1Department of Mathematical Sciences, Universiti Teknologi Malaysia, 81310 Johor Bahru,

Johor, Darul Takzim, Malaysia

 

2Department of Statistics, Institut Teknologi Sepuluh Nopember, 60111 Surabaya

Indonesia

 

3School of Environmental and Natural Resource Sciences, Universiti Kebangsaan Malaysia

43600 Bangi, Selangor Darul Ehsan, Malaysia

 

Received: 5 October 2015/Accepted: 17 March 2016

 

ABSTRACT

The air pollution index (API) has been recognized as one of the important air quality indicators used to record the correlation between air pollution and human health. The API information can help government agencies, policy makers and individuals to prepare precautionary measures in order to eliminate the impact of air pollution episodes. This study aimed to verify the monthly API trends at three different stations in Malaysia; industrial, residential and sub-urban areas. The data collected between the year 2000 and 2009 was analyzed based on time series forecasting. Both classical and modern methods namely seasonal autoregressive integrated moving average (SARIMA) and fuzzy time series (FTS) were employed. The model developed was scrutinized by means of statistical performance of root mean square error (RMSE). The results showed a good performance of SARIMA in two urban stations with 16% and 19.6% which was more satisfactory compared to FTS; however, FTS performed better in suburban station with 25.9% which was more pleasing compared to SARIMA methods. This result proved that classical method is compatible with the advanced forecasting techniques in providing better forecasting accuracy. Both classical and modern methods have the ability to investigate and forecast the API trends in which can be considered as an effective decision-making process in air quality policy.

 

Keywords: Air pollution index; ARIMA; forecasting; fuzzy time series; time series

 

ABSTRAK

Indeks pencemaran udara (IPU) penting sebagai petunjuk asas kualiti udara yang berkait rapat antara pencemaran udara dan kesihatan manusia. Maklumat IPU boleh membantu agensi kerajaan, penggubal dasar serta orang perseorangan untuk menyediakan langkah berjaga-jaga untuk mengatasi pencemaran udara. Kajian ini bertujuan untuk menganalisis trend IPU bulanan di tiga buah stesen yang berbeza di Malaysia; industri, perumahan dan pinggir bandar. Data antara tahun 2000 dan 2009 telah dianalisis berdasarkan siri ramalan masa. Kedua-dua kaedah klasik dan moden iaitu autoregresif bermusim bersepadu purata (SARIMA) dan siri masa kabur (FTS) telah diaplikasikan. Model ramalan dibandingkan melalui prestasi statistik punca min ralat kuasa dua (RMSE). Hasil kajian menunjukkan SARIMA meramal dengan baik di dua stesen bandar dengan 16% dan 19.6% yang lebih memuaskan berbanding FTS; Walau bagaimanapun, ramalan FTS lebih baik di stesen pinggir bandar dengan 25.9% lebih tepat berbanding dengan kaedah SARIMA. Keputusan ini membuktikan bahawa kaedah klasik mampu meramal dengan baik standing dengan teknik ramalan yang moden. Kedua-dua kaedah klasik dan moden mempunyai keupayaan untuk mengkaji dan meramal trend IPU dan boleh membantu dalam proses membuat keputusan yang berkesan dalam membentuk dasar kualiti udara.

 

Kata kunci: ARIMA; indeks pencemaran udara; ramalan; siri masa; siri masa kabur

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*Corresponding author; email: mhl@utm.my

 

 

 

 

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