 |
 |
 |
 |
 |
 |
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 REFERENCES Afroz,
R., Hassan, M.N. & Ibrahim, N.A. 2003. Review of air pollution
and health impacts in Malaysia. Environmental Research 92(2):
71-77. Brunelli,
U., Piazza, V., Pignato, L., Sorbello, F. & Vitabile, S. 2007.
Two-days ahead prediction of daily maximum concentrations of SO2,
O3, PM10, NO2, CO in the urban area of Palermo, Italy. Atmospheric
Environment 41(14): 2967-2995. Chaloulakou,
A., Saisana, M. & Spyrellis, N. 2003. Comparative assessment
of neural networks and regression models for forecasting summertime
ozone in Athens. Science of The Total Environment 313(1-3):
1-13. Chen,
S.M. 1996. Forecasting enrollments based on fuzzy time series.
Fuzzy Sets and Systems 81(3): 311-319. Cheng,
C.H., Chen, T.L., Teoh, H.J. & Chiang, C.H. 2008. Fuzzy time-series
based on adaptive expectation model for TAIEX forecasting. Expert
Systems with Applications 34(2): 1126-1132. Cryer,
J.D. 1986. Time Series Analysis. 1st ed. United States
of America: Duxbury Press. Department
of Environment (DoE). 2011. Malaysia Environment Quality Report
2010. Putrajaya: Department of Environment, Ministry of Sciences,
Technology and Environment, Malaysia. Department
of Environment (DoE). 2005. Malaysia Environment Quality Report
2004. Putrajaya: Department of Environment, Ministry of Sciences,
Technology and Environment, Malaysia. Hanke,
J.E. & Wichern, D.W. 2005. Business Forecasting. 8th
ed. Upper Saddle River, N.J: Pearson/Prentice Hall. Hassanzadeh,
S., Hosseinibalam, F. & Alizadeh, R. 2009. Statistical models
and time series forecasting of sulfur dioxide: A case study Tehran.
Environmental Monitoring and Assessment 155(1): 149-155.
Heo, J.S. & Kim, D.S. 2004. A new method of ozone forecasting
using fuzzy expert and neural network systems. Science of the
Total Environment 325(1-3): 221-237. Huarng, K. 2001. Effective lengths of intervals to improve forecasting
in fuzzy time series. Fuzzy Sets and Systems 123(3): 387-394.
Khashei, M. &
Bijari, M. 2010. An artificial neural network (p, d, q) model
for time series forecasting. Expert Systems with Applications
37(1): 479-489. Kumar, A. &
Goyal, P. 2011. Forecasting of daily air quality index in Delhi.
Science of the Total Environment 409(24): 5517-5523. Kumar, U. &
Jain, V. 2010. ARIMA forecasting of ambient air pollutants (O3,
NO, NO2 and CO). Stochastic Environmental Research and Risk
Assessment 24(5): 751-760. Kurt, A. &
Oktay, A.B. 2010. Forecasting air pollutant indicator levels with
geographic models 3 days in advance using neural networks. Expert
Systems with Applications 37(12): 7986-7992. Makridakis, S.,
Chatfield, C., Hibon, M., Lawrence, M., Mills, T., Ord, K. &
Simmons, L.F. 1993. The M2-competition: A real-time judgmentally
based forecasting study. International Journal of Forecasting
9(1): 5-22. Makridakis, S.,
Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski,
R., Newton, J., Parzen, E. & Winkler, R. 1982. The accuracy
of extrapolation (time series) methods: Results of a forecasting
competition. Journal of Forecasting 1(2): 111-153. Muhammad Hisyam
Lee, Nur Haizum Abd Rahman, Suhartono, Mohd Talib Latif, Maria
Elena Nor & Nur Arina Bazilah Kamisan. 2012. Seasonal ARIMA
for forecasting air pollution index: A case study. American
Journal of Applied Sciences 9(4): 570-578. Nurulilyana Sansuddin,
Nor Azam Ramli, Ahmad Shukri Yahaya, Noor Faizah Fitri Md Yusof,
Ghazali & Wesam Ahmed Al Madhoun. 2011. Statistical analysis
of PM10 concentrations at different locations in Malaysia. Environmental
Monitoring and Assessment 180(1): 573-588. Nurul Adyani Ghazali,
Nor Azam Ramli, Ahmad Shukri Yahaya, Noor Faizah Fitri Md Yusof,
Nurulilyana Sansuddin & Wesam Ahmed Al Madhoun. 2010. Transformation
of nitrogen dioxide into ozone and prediction of ozone concentrations
using multiple linear regression techniques. Environmental
Monitoring and Assessment 165(1): 475-489. Rizzo, A. &
Glasson, J. 2012. Iskandar Malaysia. Cities 29(6): 417-427.
Song, Q. &
Chissom, B.S. 1993a. Forecasting enrollments with fuzzy time series
- Part I. Fuzzy Sets and Systems 54(1): 1-9. Song, Q. &
Chissom, B.S. 1993b. Fuzzy time series and its models. Fuzzy
Sets and Systems 54(3): 269-277. Vlachogianni, A.,
Kassomenos, P., Karppinen, A., Karakitsios, S. & Kukkonen,
J. 2011. Evaluation of a multiple regression model for the forecasting
of the concentrations of NOx and PM10 in Athens and Helsinki.
Science of the Total Environment 409(8): 1559-1571. Wang, X.K. &
Lu, W.Z. 2006. Seasonal variation of air pollution index: Hong
Kong case study. Chemosphere 63(8): 1261- 1272. Yu, H.K. 2005.
Weighted fuzzy time series models for TAIEX forecasting. Physica
A: Statistical Mechanics and Its Applications 349(3-4): 609-624.
*Corresponding author; email: mhl@utm.my
|
|||
|
