|
UPSI Digital Repository (UDRep)
|
|
| ||||||||||||||||
| Abstract : |
| River flow prediction is significantly related to urban hydrology impact which can provide information to solve any problems such as flood in urban area. The daily river flow of Klang River, Malaysia was chosen to be forecasted in this pilot study which based on phase space reconstruction. 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 Cao method. The results from the reconstruction of phase space have been used in the forecasting process using local linear approximation method. From our investigation, river flow at Klang River is chaotic based on the analysis from Cao method. The overall results provide good value of correlation coefficient. The value of correlation coefficient is acceptable since the area of the case study is influence by a lot of factors. Therefore, this pilot study may be proposed to forecast daily river flow data with the purpose of providing information about the flow of the river system in urban area. |
| References |
1. Hall, M.J. Urban hydrology. (1984) . Cited 74 times. ISBN: 0853342687; 978-085334268-7 2. Sivakumar, B. Chaos theory in hydrology: Important issues and interpretations (2000) Journal of Hydrology, 227 (1-4), pp. 1-20. Cited 207 times. doi: 10.1016/S0022-1694(99)00186-9 3. Ghorbani, M.A., Kisi, O., Aalinezhad, M. A probe into the chaotic nature of daily streamflow time series by correlation dimension and largest Lyapunov methods (Open Access) (2010) Applied Mathematical Modelling, 34 (12), pp. 4050-4057. Cited 37 times. doi: 10.1016/j.apm.2010.03.036 4. She, D.X., Yang, X. (2010) Mathematical Problems in Engineering, pp. 1-16. 2010. 5. Khatibi, R., Sivakumar, B., Ghorbani, M.A., Kisi, O., Koçak, K., Farsadi Zadeh, D. Investigating chaos in river stage and discharge time series (2012) Journal of Hydrology, 414-415, pp. 108-117. Cited 55 times. doi: 10.1016/j.jhydrol.2011.10.026 6. Tongal, H. Nonlinear forecasting of stream flows using a chaotic approach and artificial neural networks (2013) Earth Sciences Research Journal, 17 (2), pp. 119-126. Cited 8 times.http://www.revistas.unal.edu.co/index.php/ esrj/article/download/37073/44473 7. Sivakumar, B. A phase-space reconstruction approach to prediction of suspended sediment concentration in rivers (2002) Journal of Hydrology, 258 (1-4), pp. 149-162. Cited 84 times. doi: 10.1016/S0022-1694(01)00573-X 8. Sivakumar, B. Forecasting monthly streamflow dynamics in the western United States: A nonlinear dynamical approach (2003) Environmental Modelling and Software, 18 (8-9), pp. 721-728. Cited 43 times. www.elsevier.com/inca/publications/store/4/2/2/9/2/1 doi: 10.1016/S1364-8152(03)00074-4 9. Adenan, N.H., Noorani, M.S.M. (2014) Sains Malaysiana, pp. 463-471. 10. Cao, L. Practical method for determining the minimum embedding dimension of a scalar time series (1997) Physica D: Nonlinear Phenomena, 110 (1-2), pp. 43-50. Cited 1100 times. doi: 10.1016/S0167-2789(97)00118-8 11. Hosking, J.R.M., Wallis, J.R. (2005) Regional Frequency Analysis: An Approach Based on L-Moments, p. 244. Cited 1706 times. (Cambridge University Press) |
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |