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Downstream Prediction Using a Nonlinear Prediction Method : Volume 10, Issue 11 (22/11/2013)

By Adenan, N. H.

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Book Id: WPLBN0004011596
Format Type: PDF Article :
File Size: Pages 24
Reproduction Date: 2015

Title: Downstream Prediction Using a Nonlinear Prediction Method : Volume 10, Issue 11 (22/11/2013)  
Author: Adenan, N. H.
Volume: Vol. 10, Issue 11
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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M. Nooran, M. S., & Adenan, N. H. (2013). Downstream Prediction Using a Nonlinear Prediction Method : Volume 10, Issue 11 (22/11/2013). Retrieved from

Description: Universiti Pendidikan Sultan Idris, Perak, Malaysia. The estimation of river flow is significantly related to the impact of urban hydrology, as this could provide information to solve important problems, such as flooding downstream. The nonlinear prediction method has been employed for analysis of four years of daily river flow data for the Langat River at Kajang, Malaysia, which is located in a downstream area. The nonlinear prediction method involves two steps; namely, the reconstruction of phase space and prediction. The reconstruction of phase space involves reconstruction from a single variable to the m-dimensional phase space in which the dimension m is based on optimal values from two methods: the correlation dimension method (Model I) and false nearest neighbour(s) (Model II). The selection of an appropriate method for selecting a combination of preliminary parameters, such as m, is important to provide an accurate prediction. From our investigation, we gather that via manipulation of the appropriate parameters for the reconstruction of the phase space, Model II provides better prediction results. In particular, we have used Model II together with the local linear prediction method to achieve the prediction results for the downstream area with a high correlation coefficient. In summary, the results show that Langat River in Kajang is chaotic, and, therefore, predictable using the nonlinear prediction method. Thus, the analysis and prediction of river flow in this area can provide river flow information to the proper authorities for the construction of flood control, particularly for the downstream area.

Downstream prediction using a nonlinear prediction method

Sivakumar, B.: Chaos theory in hydrology: important issues and interpretation, J. Hydrol., 227, 1–20, 2000.; Abarbanel, H. D. I.: Analysis of observed chaotic data, Springer-Verlag, Inc, New York, 1996.; Ahmad, Z. and Juahir, H.: Neural Network Model for Prediction of Discharged from the Catchments of Langat River, Malaysia, IIUM Engineering Journal, 7, 25–34, 2006.; Adenan, N. H. and Noorani, M. S. M.: Behaviour of daily river flow: Chaotic?, Proceedings of the 20th National Symposium on Mathematical Sciences: Research in Mathematical Sciences: A Catalyst for Creativity and Innovation, Malaysia, 221–228, 2013.; Department of Irrigation and Drainage Malaysia: Langat River Integrated River Basin Management Study, Final Report, Technical Studies Part 1 of 4, 2005.; Department of Irrigation and Drainage Malaysia: Review of the National Water Resources (2000–2050) and Formulation of National Water Resources Policy, Selangor, Federal Territory of Kuala Lumpur and Putrajaya, 2011.; Ghani, A. A., Ali, R., Zakaria, N. A., Hasan, Z. A., Chang, C. K., and Ahmad, M. S. S.: A Temporal Change Study of the Muda River System Over 22 Years, International Journal of River Basin Management, 8, 25–37, 2010.; Sangoyomi, A., Lall, L., and Abarbanel, H. D. I.: Nonlinear dynamics of the Great Salt Lake: dimension estimation, Water Resour. Res., 32, 149–159, 1996.; Ghorbani, M. A., Kisi, O., and Aalinezhad, M.: A probe into the chaotic nature of daily streamflow time series by correlation dimension and largest Lyapunov methods, Appl. Math. Modell., 34, 4050–4057, 2010.; Ghorbani, M. A., Daneshfaraz, R., Arvanagi, H., and Pourzangbar, A.: Local Prediction in River Discharge Time Series, Online Journal of Civil Engineering and Urbanism, 2, 51–55, 2012.; Grassberger, P.: Do climatic attractors exist?, Nature, 323, 609–612, 1986.; Hall, M. J.: Urban Hydrology, Elsevier Applied Science Publishers, New York, 1984.; Islam, M. N. and Sivakumar, B.: Characterization and prediction of runoff dynamics: a nonlinear dynamical view, Adv. Water Res., 25, 179–190, 2002.; Jayawardena, A. W. and Lai, F.: Chaos in hydrological time series, Proceeding of the Yokohama Symposium – Extreme Hydrological Events: Precipitation, Floods and Droughts, Yokohama, 59–66, 1993.; Jayawardena, A. W. and Lai, F.: Analysis and prediction of chaos in rainfall and streamflow time series, J. Hydrol., 153, 23–52, doi:10.1016/0022-1694(94)90185-6, 1994.; Juahir, H., Zain, S. M., Yusoff, M. K., Hanidza, T. I. T., Armi, A. S. M., Toriman, M. E., and Mokhtar, M.: Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques, Environ. Monit. Assess., 173, 625–641, 2011.; Khatibi, R., Sivakumar, B., Ghorbani, M. A., Kisi, O., Kocak, K., and Zadeh, D. F.: Investigating chaos in river stage and discharge time series, J. Hydrol., 414–415, 108–117, 2012.; Lorenz, E. N.: Atmospheric predictability as revealed by naturally occurring analogues, J. Atmos. Sci, 26, 636–646, 1969.; Men, B., Zhao, X., and Liang, C.: Chaotic analysis on monthly precipitation on Hills Region in Middle Sichuan of China, Nature and Science, 2, 45–51, 2004.; Martins, O. Y., Sadeeq, M. A., and Ahaneku, I. E.: Nonlinear Deterministic Chaos in Beneu River Flow Daily TIme Sequence, Journal of Water Resource and Protection, 3, 747–757, 2011.; Mohammed, T. A., Al-Hassoun, S., and Ghazali, A. H.: Prediction of flood levels a streacth of the Langat River with insufficient hydrological data, Pertanika Journal of Science & Technology, 2, 237–248, 2011.; Regonda, S., Rajagopalan, B., Lall, U., Clark, M., and Moon, Y.-I.: Local polynomial method for ensemble forecast of time series, Nonlin. Processes Geophys., 12, 397–406, doi:10.5194/npg-12-397-2005, 2005.; Shabri, A. and Suhartono: Streamflow forecasting using least-squares support vector machines, Hydrolog. Sci. J


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