River Flow Prediction Using Nonlinear Prediction Method

Authors: N. H. Adenan, M. S. M. Noorani

Abstract:

River flow prediction is an essential to ensure proper management of water resources can be optimally distribute water to consumers. This study presents an analysis and prediction by using nonlinear prediction method involving monthly river flow data in Tanjung Tualang from 1976 to 2006. Nonlinear prediction method involves the reconstruction of phase space and local linear approximation approach. The phase space reconstruction involves the reconstruction of one-dimensional (the observed 287 months of data) in a multidimensional phase space to reveal the dynamics of the system. Revenue of phase space reconstruction is used to predict the next 72 months. A comparison of prediction performance based on correlation coefficient (CC) and root mean square error (RMSE) have been employed to compare prediction performance for nonlinear prediction method, ARIMA and SVM. Prediction performance comparisons show the prediction results using nonlinear prediction method is better than ARIMA and SVM. Therefore, the result of this study could be used to develop an efficient water management system to optimize the allocation water resources.

Keywords: River flow, nonlinear prediction method, phase space, local linear approximation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1088990

References:

1] J. R. M. Hosking and J.R. Wallis, Regional Frequency Analysis: An Approach Based on L-Moments. Cambridge University Press. UK, 1997.
[2] A. Jain and A.M. Kumar, “Hybrid neural network models for hydrologic time series forecasting,” Applied Soft Computing, vol. 7, pp. 585-592, 2007.
[3] A. Shabri and Suhartono, “Streamflow Forecasting using Least-squares Support Vector Machines,” Hydrological Sciences Journal, vol. 57, pp. 1275-1293, 2012.
[4] J.D. Salas, G.Q. TabiosIII and P. Bartolini, “Approaches to Multivariate Modeling of Water Resources Time Series,” Water Resource Bulletin, vol. 4, pp. 683-708, 1985.
[5] H.M. Azamatulla, A.A. Ghani, C.S. Leow, C.K. Chang and N.A. Zakaria, “Gene-Expression Programming for the Development of a Stage-Discharge Curve of the Pahang River,” Water Resource Management, vol. 25, pp. 2901-2916, 2011.
[6] Y. Mohd Shafiek, Dr., J. Hishamuddin and H. Sobri, “Daily streamflow forecasting using simplified rule-based fuzzy logic system,” The Journal of the Institution of Engineers, Malaysia, vol. 66, pp. 23-28, 2005.
[7] A.A. Ghani, R. Ali, N.A. Zakaria, Z.A. Hasan, C.K. Chang and M.S.S. Ahamad, “A Temporal Change Study of the Muda River System Over 22 Years,” International Journal of River Basin Management, vol. 8, pp. 25-37, 2010.
[8] Z.A. Zakaria and A. Shabri, “Streamflow Forecasting at Ungaged Sites Using Support Vector Machines,” Applied Mathematical Sciences, vol. 6, pp. 3003-3014, 2012.
[9] Department of Irrigation and Drainage Malaysia, Review of National Water Resources (2000-2050) and Formulation Water Resources Policy. 2011. Kuala Lumpur.
[10] A.W. Jayawardena and F. Lai, “Analysis and prediction of chaos in rainfall and streamflow time series,” Journal of Hydrology, vol. 153, pp. 23-52, 1994.
[11] W. Wang, P.H. Van Gelder and J. Vrijling, “Is the streamflow process chaotic?,” in International Symposium Stochastic Hydraulic, Madrid, Spain 2005 pp. 162-164.
[12] W.W. Ng, US.Panu and W.C. Lennox, “Chaos based analytical techniques for daily extreme hydrological observations,” Journal of Hydrology, vol. 842, pp. 17-41, 2007.
[13] B. Sivakumar, “A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers,” Journal of Hydrology, vol. 258, pp. 149-162, 2002.
[14] B. Sivakumar, “Forecasting monthly streamflow dynamics in the western United States: a nonlinear dynamical approach,” Environmental Modelling & Software, vol. 18, pp. 721-728, 2003.
[15] R. Khatibi, B. Sivakumar, M.A. Ghorbani, O. Kisi, K. Kocak and D.F. Zadeh, “Investigating chaos in river stage and discharge time series,” Journal of Hydrology, vol. 414-415, pp. 108-117, 2012.
[16] A. Sangoyomi, L. Lall and H.D.I. Abarbanel, “Nonlinear Dynamics of the Great Salt Lake: Dimension Estimation,” Water Resource Research, vol. 32, pp. 149-159, 1996.
[17] M.N. Islam and B. Sivakumar, “Characterization and prediction of runoff dynamics: a nonlinear dynamical view,” Advances in Water Resources, vol. 25, pp. 179–190, 2002.
[18] N.M. Gazzaz, M.K. Yusoff, M.F. Ramli, A.Z. Aris and H. Juahir, “Characterization of spatial patterns in river water quality using chemometric pattern recognition techniques,” Marine Pollution Bulletin, vol. 64, pp. 688-698, 2012.
[19] M. Casdagli, “Nonlinear prediction of chaotic time series,” Physica D: Nonlinear Phenomena, vol. 35, pp. 335–356, 1989.