Sains Malaysiana 46(8)(2017): 1333–1339

http://dx.doi.org/10.17576/jsm-2017-4608-20

 

Aplikasi Model Baharu Penambahbaikan Pendekatan Kalut ke atas Peramalan Siri Masa Kepekatan Ozon

(New Improved Chaotic Approach Model Application on Forecasting Ozone Concentration Time Series)

 

NOR ZILA ABD HAMID1* & MOHD SALMI MD NOORANI2

 

1Jabatan Matematik, Fakulti Sains dan Matematik, Universiti Pendidikan Sultan Idris, 35900 Tanjung Malim, Perak Darul Ridzuan, Malaysia

 

2Pusat Pengajian Sains Matematik, Fakulti Sains dan Teknologi, 43600 UKM Bangi, Selangor Darul Ehsan, Malaysia

 

Diserahkan: 22 Julai 2016/Diterima: 13 Januari 2017

 

ABSTRAK

Kajian ini merupakan aplikasi pendekatan kalut ke atas peramalan siri masa bahan pencemar udara ozon di stesen asas Malaysia yang terletak di Jerantut, Pahang. Sebelum model peramalan dibina, siri masa diuji terlebih dahulu sama ada bersifat kalut atau tidak. Melalui plot ruang fasa dan kaedah Cao, siri masa bahan pencemar ozon didapati bersifat kalut bermatra rendah. Oleh itu, model peramalan melalui kaedah penghampiran linear setempat dibina. Sebagai inovasi, model ini ditambah baik. Sebagai perbandingan, model peramalan regresi linear turut dibina. Melalui pengiraan purata ralat mutlak, ralat punca purata kuasa dua dan pekali korelasi, keputusan menunjukkan bahawa model baharu penambahbaikan penghampiran linear setempat adalah lebih baik berbanding model-model yang lain. Maka, penambahbaikan yang dilakukan adalah berbaloi. Dengan itu, pendekatan kalut adalah pendekatan alternatif yang sesuai digunakan bagi membangunkan model peramalan siri masa bahan pencemar ozon. Penemuan model baharu dalam kajian ini diharap dapat membantu memudahkan usaha pihak-pihak berkepentingan dalam menguruskan isu pencemaran udara, khususnya ozon.

 

Kata kunci: Kaedah penghampiran setempat; Malaysia; ozon; pendekatan kalut; peramalan

 

ABSTRACT

This study is an application of chaotic approach on forecasting the ozone air pollutant time series at Malaysian background station located in Jerantut, Pahang. Before the forecasting model can be built, the time series are tested in advance whether the nature is chaotic or not. Through phase space plot and Cao method, the ozone air pollutant time series were found to be low in dimensional chaotic. Therefore, the forecasting model through local linear approximation is constructed. As an innovation, this model is improved. As comparison, the linear regression forecasting model was also constructed. By calculating the mean absolute error, root mean square error and correlation coefficient, the results showed that the new improved local linear approximation model is better than the other models. Thus, the improvement was worth it. Therefore, chaotic approach is an alternative approach that can be used to contruct forecasting model for ozone pollutants time series. The discovery of new method in this study is expected to help facilitate the efforts of stakeholders in dealing with the issues of air pollution, especially ozone.

 

Keywords: Chaotic approach; forecasting; local approximation method; Malaysia; ozone

RUJUKAN

Abarbanel, H.D.I. 1996. Analysis of Observed Chaotic Data. New York: Springer-Verlag.

Adenan, N.H. & Noorani, M.S.M. 2014. Nonlinear prediction of river flow in different watershed acreage. KSCE Journal of Civil Engineering 18(7): 2268-2274. doi:10.1007/s12205- 014-0646-4.

Adenan, N.H. & Noorani, M.S.M. 2015. Predicting time series data at floodplain area using chaos approach. Sains Malaysiana44(3): 463-471.

Awang, N.R., Elbayoumi, M., Ramli, N.A. & Yahaya, A.S. 2015. Diurnal variations of ground-level ozone in three port cities in Malaysia. Air Qual Atmos Health. doi:10.1007/s11869- 015-0334-7.

Banan, N., Latif, M.T., Juneng, L. & Ahamad, F. 2013. Characteristics of surface ozone concentrations at stations with different backgrounds in the Malaysian Peninsula. Aerosol and Air Quality Research 13: 1090-1106. doi:10.4209/ aaqr.2012.09.0259.

Cakmak, S., Hebbern, C., Vanos, J., Crouse, D.L. & Burnett, R. 2016. Ozone exposure and cardiovascular-related mortality in the Canadian Census Health and Environment Cohort (CANCHEC) by spatial synoptic classification zone. Environmental Pollution 214(2): 589-599. doi:10.1016/j. envpol.2016.04.067.

Cao, L. 1997. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D 110: 43-50.

Chattopadhyay, G. & Chattopadhyay, S. 2008. A probe into the chaotic nature of total ozone time series by correlation dimension method. Soft Computing 12: 1007-1012. doi:10.1007/s00500-007-0267-7.

Chelani, A.B. 2010. Nonlinear dynamical analysis of ground level ozone concentrations at different temporal scales. Atmospheric Environment 44(34): 4318-4324. doi:10.1016/j. atmosenv.2010.07.028.

Chen, J., Islam, S. & Biswas, P. 1998. Nonlinear dynamics of hourly ozone concentrations: Nonparametric short term prediction. Atmospheric Environment 32(11): 1839-1848.

Cuculeanu, V., Rada, C. & Lupu, A. 2009. Study on the geometrical and dynamical characteristics of the Arosa ozone series attractor. Geophysique 52-53: 77-85.

Das, A., Das, P. & Çoban, G. 2012. Chaotic analysis of the foreign exchange rates during 2008 to 2009 recession. African Journal of Business Management 6(15): 5226-5233. doi:10.5897/AJBM11.2682.

Domenico, M.D., Ali, M., Makarynskyy, O. & Makarynska, D. 2013. Chaos and reproduction in sea level. Applied Mathematical Modelling 37(6): 3687-3697. doi:10.1016/j. apm.2012.08.018.

Frazier, C. & Kockelman, K.M. 2004. Chaos theory and transportation systems: An instructive example. Transportation Research 1897: 9-17.

Ghazali, N.A., Ramli, N.A., Yahaya, A.S., Yusof, N.F.F.M., Sansuddin, N. & Madhoun, W.A.A. 2010. Transformation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques. Environ. Monit. Assess. 165: 475-489. doi:10.1007/s10661- 009-0960-3.

Hamid, N.Z.A. & Noorani, M.S.M. 2013. An improved prediction model of ozone concentration time series based on chaotic approach. International Journal of Mathematical, Computational Science and Engineering 7(11): 206-211.

Hamid, N.Z.A. & Noorani, M.S.M. 2014. A pilot study using chaotic approach to determine characteristics and forecasting of PM10 concentration time series. Sains Malaysiana43(3): 475-481.

Ismail, M., Abdullah, S., Yuen, F.S. & Ghazali, N.A. 2016. A ten-year investigation on ozone and it precursors at Kemaman, Terengganu, Malaysia. Environmental Asia 9(1): 1-8. doi:10.14456/ea.1473.1.

Kocak, K., Saylan, L. & Sen, O. 2000. Nonlinear time series prediction of O3 concentration in Istanbul. Atmospheric Environment 34: 1267-1271.

Lakshmi, S.S. & Tiwari, R.K. 2009. Model dissection from earthquake time series: A comparative analysis using modern non-linear forecasting and artificial neural network approaches. Computers & Geosciences 35: 191-204. doi:10.1016/j.cageo.2007.11.011.

Mabrouk, M.S. 2011. A nonlinear pattern recognition of pandemic H1N1 using a state space based methods. Avicenna Journal of Medical Biotechnology 3(1): 25-29.

Madaniyazi, L., Nagashima, T., Guo, Y., Pan, X. & Tong, S. 2016. Projecting ozone-related mortality in East China. Environment International 92-93: 165-172. doi:10.1016/j. envint.2016.03.040.

Muhamad, M., Ul-saufie, A.Z. & Deni, S.M. 2015. Three days ahead prediction of daily 12 hour ozone (O3) concentrations for urban area in Malaysia. Journal of Environmental Science and Technology 8(3): 102-112. doi:10.3923/ jest.2015.102.112.

Norazian, M.N., Shukri, Y.A., Azam, R.N. & Bakri, A.M.M.A. 2008. Estimation of missing values in air pollution data using single imputation techniques. ScienceAsia 34: 341-345. doi:10.2306/scienceasia1513-1874.2008.34.341.

Petkov, B.H., Vitale, V., Mazzola, M., Lanconelli, C. & Lupi, A. 2015. Chaotic behaviour of the short-term variations in ozone column observed in Arctic. Commun. Nonlinear Sci. Numer. Simulat.26(1-3): 238-249. doi:10.1016/j.cnsns.2015.02.020.

Sivakumar, B. 2002. A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers. Journal of Hydrology 258: 149-162.

Sivakumar, B., Liong, S.Y., Liaw, C.Y. & Phoon, K.K. 1999. Singapore rainfall behaviour: Chaotic? Journal of Hydrologic Engineering 4(1): 38-48.

Sprott, J.C. 2003. Chaos and Time-Series Analysis. Oxford: Oxford University Press.

Tan, K.C., Lim, H.S. & Jafri, M.Z.M. 2016. Prediction of column ozone concentrations using multiple regression analysis and principal component analysis techniques: A case study in peninsular Malaysia. Atmospheric Pollution Research 7(3): 533-546. doi:10.1016/j.apr.2016.01.002.

Toh, Y.Y., Lim, S.F. & von Glasow, R. 2013. The influence of meteorological factors and biomass burning on surface ozone concentrations at Tanah Rata, Malaysia. Atmospheric Environment 70: 435-446. doi:10.1016/j. atmosenv.2013.01.018.

 

 

*Pengarang untuk surat-menyurat; email: nor_zila@yahoo.com

 

 

 

 

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