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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 Diserahkan: 5 Oktober 2015/Diterima: 17 Mac 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
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*Pengarang untuk surat-menyurat; email: mhl@utm.my
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