Sains Malaysiana 44(3)(2015): 463–471

Peramalan Data Siri Masa Aliran Sungai di Dataran Banjir dengan Menggunakan

Pendekatan Kalut

(Predicting Time Series Data at Floodplain Area using Chaos Approach)

NUR HAMIZA ADENAN¹* & MOHD SALMI MD NOORANI²

¹Jabatan Matematik, Fakulti Sains dan Matematik, Universiti Pendidikan Sultan Idris

35900 Tanjong Malim, Perak Darul Ridzuan, Malaysia

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

Diserahkan: 22 Mei 2014/Diterima: 18 Ogos 2014

ABSTRAK

Bencana banjir boleh menjejaskan kehidupan dan harta benda. Risiko kejadian banjir boleh diminimumkan jika amaran awal dapat dikeluarkan. Di atas inisiatif ini, peramalan aliran sungai harian dijalankan di sebuah stesen aliran sungai di Sungai Muda, Malaysia yang terletak di dataran banjir. Peramalan dengan mengaplikasikan pendekatan kalut melibatkan dua langkah iaitu pembinaan semula ruang fasa dan peramalan. Pembinaan ruang fasa melibatkan satu pemboleh ubah iaitu data aliran sungai yang dibina semula kepada m-dimensi dengan menggunakan nilai optimum dimensi pembenaman daripada kaedah Cao dan variasi nilai dimensi pembenaman untuk pendekatan songsang. Hasil daripada pembinaan ruang fasa ini digunakan untuk meramal aliran sungai dengan menggunakan kaedah peramalan setempat. Hasil kajian menunjukkan data aliran Sungai Muda adalah bertelatah kalut berdasarkan analisis daripada kaedah Cao. Keseluruhan hasil peramalan bagi kedua-dua kaedah dapat memberikan peramalan yang baik berdasarkan pekali korelasi yang tinggi. Namun, kombinasi parameter asas bagi pendekatan songsang memberikan hasil peramalan yang lebih baik. Oleh itu, pendekatan songsang boleh dicadangkan bagi meramal data aliran sungai harian dengan tujuan memberikan maklumat penting mengenai sistem aliran sungai di dataran banjir terutamanya di Sungai Muda.

Kata kunci: Aliran sungai; dataran banjir; data siri masa; pendekatan kalut; peramalan

ABSTRACT

Floods are natural disaster that can cause substantial losses of lives and property. Flood risk can be minimized if an early warning can be issued. In this regard, daily river flow prediction was analyzed at a river flow station in Ladang Victoria, Malaysia which is located in a floodplain area. Prediction using chaotic approach that involves the reconstruction of phase space and prediction have been employed in this research. The reconstruction of phase space involves a single variable of river flow data to m-dimensional phase space in which the dimension (m) is based on the optimal values of method of Cao and the variation of m for inverse approach. The results from the reconstruction of phase space have been used in the prediction process using local linear approximation method. From our investigation, river flow at Muda River is chaotic based on the analysis from Cao method. Overall, prediction results for both methods can provide a good prediction based on a high correlation coefficient. However, the combination of the preliminary parameters for the inverse approach yields better prediction. Therefore, the inverse approach can be proposed for predicting daily river flow data for the purpose of providing important information about the flow of the river system in floodplain area especially in Sungai Muda.

Keywords: Chaos approach; floodplain area; prediction; river flow; time series data

RUJUKAN

Abarbanel, H. 1996. Analysis of Observed Chaotic Data. New York: Springer. hlm. 272.

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.

Adenan, N.H. & Noorani, M.S.M. 2013. Monthly river flow prediction using a nonlinear prediction method. International Journal of Mathematical, Computational Science and Engineering 7(11): 62-66.

Azamathulla, H.M. & Zahiri, A. 2012. Flow discharge prediction in compound channels using linear genetic programming. Journal of Hydrology 454-455: 203-207.

Bernama. 2010, November 3. Floods claim 2 lives; over 36,000 evacuated. The Sun Daily, http://www.thesundaily.my/ node/136603.

Bernama. 2009, November 16. Floods in Kedah and Perak worsen. The Sun Daily, http://www.thesundaily.my/ node/149157.

Bernama. 2005, December 21. Alor Star airport close due to flood. The Sun Daily, http://www.thesundaily.my/node/175577.

Box, G.E.P., Jenkins, G.M. & Reinsel, G.C. 1976. Time Series Analysis: Forecasting and Control. Chichester: Wiley. hlm.784.

Cao, L. 1997. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D: Nonlinear Phenomena 110(1-2): 43-50.

Domenico, M.D., Ghorbani, M.A., Makarynskyy, O., Makarynska, D. & Asadi, H. 2013. Chaos and reproduction in sea level. Applied Mathematical Modelling 37(6): 3687-3697.

Feng, L.H. & Lu, J. 2010. The practical research on flood forecasting based on artificial neural networks. Expert Systems with Applications 37(4): 2974-2977.

Fojt, O. & Holcik, J. 1998. Applying nonlinear dynamics to ECG signal processing. IEEE Engineering in Medicine and Biology Magazine 17(2): 96-101.

Ghani, A.A., Chang, C.K., Leow, C.S. & Zakaria, N.A. 2012. Sungai Pahang digital flood mapping: 2007 flood. International Journal of River Basin Management 10(2): 139-148.

Islam, M. & Sivakumar, B. 2002. Characterization and prediction of runoff dynamics: A nonlinear dynamical view. Advances in Water Resources 25(2): 179-190.

Jayawardena, A.W. & Gurung, A.B. 2000. Noise reduction and prediction of hydrometeorological time series: Dynamical systems approach vs. stochastic approach. Journal of Hydrology 228(3-4): 242-264.

Jayawardena, A.W. & Lai, F. 1994. Analysis and prediction of chaos in rainfall and stream flow time series. Journal of Hydrology 153(1-4): 23-52.

Julien, P.Y., Ghani, A.A., Zakaria, N.A., Abdullah, R., Chang, C.K. & Asce, M. 2010. Case study: Flood mitigation of the Muda River, Malaysia. Journal of Hydraulic Engineering 136(4): 251-261.

Kantz, H. & Schreiber, T. 2004. Nonlinear Time Series Analysis. Cambridge: Cambridge University Press. hlm. 369.

Lan, L.W., Kuo, A.Y. & Lin, F. 2003. Testing and prediction of traffic flow dynamics with chaos. Journal of the Eastern Asia Society for Transportation Studies 5: 1975-1990.

Lau, K.W. & Wu, Q.H. 2008. Local prediction of non-linear time series using support vector regression. Pattern Recognition 41(5): 1539-1547.

Liebert, W. & Schuster, H. 1989. Proper choice of the time delay for the analysis of chaotic time series. Physics Letters A 142(2): 107-111.

Rojas, I., Valenzuela, O., Rojas, F., Guillen, A., Herrera, L.J., Pomares, H., Marquez, L. & Pasadas, M. 2008. Soft-computing techniques and ARMA model for time series prediction. Neurocomputing 71(4-6): 519-537.

Musa, S. & Wan Mohamed, W.A. 2007. Peramalan kadaralir sungai bermusim dan tidak bermusim dengan kaedah pelicinan eksponen. Prosiding Kebangsaan Awam07: 682- 692.

Peters, E.E. 1996. Chaos and Order in the Capital Markets: A New View of Cycles, Prices, and Market Volatility. Volume 1. New York: John Wiley & Sons. hlm. 274.

Regonda, S.K., Rajagopalan, B., Lall, U., Clark, M. & Moon, Y.I. 2005. Local polynomial method for ensemble forecast of time series. Nonlinear Processes in Geophysics 12(3): 397-406.

Rodriguez-Iturbe, I., Febres De Power, B., Sharifi, M.B. & Georgakakos, K.P. 1989. Chaos in rainfall. Water Resources Research 25(7): 1667-1675.

Shabri, A. & Suhartono. 2012. Streamflow forecasting using least-squares support vector machines. Hydrological Sciences Journal 57(7): 1275-1293.

Sivakumar, B. 2003. Forecasting monthly streamflow dynamics in the western United States: A nonlinear dynamical approach. Environmental Modelling & Software 18(8-9): 721-728.

Sivakumar, B. 2002. A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers. Journal of Hydrology 258(1-4): 149-162.

Theiler, J., Eubank, S., Alamos, L., Trail, O.P. & Fe, S. 1993. Don’t bleach chaotic data. Chaos 4(1): 1-12.

Wang, W., van Gelder, P.H.A.J.M., Vrijling, J.K. & Ma, J. 2006. Forecasting daily streamflow using hybrid ANN models. Journal of Hydrology 324(1-4): 383-399.

Warren Viesman, J. & Lewis, G.L. 2008. Introduction to Hydrology. 5th ed. New Jersey: Prentice Hall. hlm.1-599.

Zainab Hashim. 2010. Development of Atmospheric Based Flood Forecasting and Warning System for Selected River Basins in Malaysia. http://www.met.gov.my/index.php?option=com_content&task= view&id=2935&Itemid=2329.

*Pengarang untuk surat-menyurat; email: nurhamiza.adenan@gmail.com