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
Received: 22
July 2016/Accepted: 13 January 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
REFERENCES
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.
*Corresponding author; email: nor_zila@yahoo.com