UPSI Digital Repository (UDRep)
|
|
|
Abstract : Universiti Pendidikan Sultan Idris |
The prediction of water level in floodplain area is important for early signals and flood control. A total of 6350 hourly water level time series data located at Sungai Dungun were used in this study. The data were divided into training set and testing set. The training set consisted of the first 6000 data which were used to predict the last 350 data. A total of six set data consisting of different amount of training set of data were involved in this study. Consequently, it was used to determine the influence of different amount of data on predicting accuracy by using chaos approach. Those sets of data required a combination of parameters for prediction. In this study, the different amount of data had impacts on the combination of parameter for prediction. In addition, the correlation coefficient showed different values for all sets of data and excellent prediction when they were all used in testing the data. Hence, the different total amount of data will give impact on different combination of parameters and prediction accuracy for water level prediction based on chaos approach in floodplain area. ? 2021 by authors, all rights reserved. |
References |
Abarbanel, H. D. I. (1996). Analysis of Observed Chaotic Data, Retrieved from www.scopus.com Adenan, N. H. (2015). Analisis Dan Peramalan Data Siri Masa Aliran Sungai Dengan Menggunakan Pendekatan Kalut, Retrieved from www.scopus.com Adenan, N. H., & Noorani, M. S. M. (2016). Multiple time-scales nonlinear prediction of river flow using chaos approach. Jurnal Teknologi, 78(7), 1-7. doi:10.11113/jt.v78.3561 Ali, N. M., Hamid, N. Z. A., & Ali, N. M. (2020). Environmental modelling through chaotic approach for malaysian west coast sea level. Paper presented at the Journal of Physics: Conference Series, , 1529(3) doi:10.1088/1742-6596/1529/3/032092 Retrieved from www.scopus.com Cao, F., Tao, Q., Dong, S., & Li, X. (2020). Influence of rain pattern on flood control in mountain creek areas: A case study of northern zhejiang. Applied Water Science, 10(10) doi:10.1007/s13201-020-01308-x 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. doi:10.1016/S0167-2789(97)00118-8 Echendu, A. J. (2020). The impact of flooding on Nigeria’s sustainable development goals (SDGs). Ecosystem Health and Sustainability, 6(1) doi:10.1080/20964129.2020.1791735 Hamid, N. Z. A. (2018). To cite this article: Nor zila abd hamid. IOP Conf.Ser.: Earth Environ.Sci, 169, 12107. Retrieved from www.scopus.com Hamid, N. Z. A., & Md Noorani, M. S. (2017). New improved chaotic approach model application on forecasting ozone concentration time series. [Aplikasi model baharu penambahbaikan pendekatan kalut ke atas peramalan siri masa kepekatan ozon] Sains Malaysiana, 46(8), 1333-1339. doi:10.17576/jsm-2017-4608-20 Huang, F., Huang, J., Jiang, S. -., & Zhou, C. (2017). Prediction of groundwater levels using evidence of chaos and support vector machine. Journal of Hydroinformatics, 19(4), 586-606. doi:10.2166/hydro.2017.102 Khatami, S. (2013). Nonlinear chaotic and trend analyses of water level at urmia lake, iran. Nonlinear Chaotic and Trend Analyses of Water Level at Urmia Lake, Iran, Retrieved from www.scopus.com Khatibi, R., Ghorbani, M. A., Aalami, M. T., Kocak, K., Makarynskyy, O., Makarynska, D., & Aalinezhad, M. (2011). Dynamics of hourly sea level at hillarys boat harbour, western australia: A chaos theory perspective. Ocean Dynamics, 61(11), 1797-1807. doi:10.1007/s10236-011-0466-8 Li, T. Y., & Yorke, J. A. (1975). Period three implies chaos. Amer.Math.Monthly, 82, 985-992. Retrieved from www.scopus.com Lorenz, E. N. (1963). Deterministic nonperiodic flow. J.Atmos.Sci., 20, 130-141. Retrieved from www.scopus.com Mashuri, A., Adenan, N. H., & Hamid, N. Z. A. (2019). Determining the chaotic dynamics of hydrological data in flood-prone area. Civil Engineering and Architecture, 7(6), 71-76. doi:10.13189/cea.2019.071408 Mihailović, D., Nikolić-Dorić, E. M., Arsenić, I., Malinović-Milićević, S., Singh, V. P., Stošić, T., & Stošić, B. (2018). Analysis of Daily Streamflow Complexity by Kolmogorov Measures and Lyapunov Exponent, Retrieved from www.scopus.com Shakti, P. C., Hirano, K., & Iizuka, S. (2020). Flood inundation mapping of the hitachi region in the kuji river basin, japan, during the october 11–13, 2019 extreme rain event. Journal of Disaster Research, 15(6), 712-725. doi:10.20965/jdr.2020.p0712 Sprott, J. C. (2003). Chaos and Time-Series Analysis, Retrieved from www.scopus.com Takens, F. (1981). Detecting strange attractors in turbulence. Lecture Notes in Mathematics, 898, 366-381. Retrieved from www.scopus.com Terengganu, J. N. (2020). Laporan Banjir 2010-2011, Retrieved from www.scopus.com Vaheddoost, B., Aksoy, H., & Abghari, H. (2016). Prediction of water level using monthly lagged data in lake urmia, iran. Water Resources Management, 30(13), 4951-4967. doi:10.1007/s11269-016-1463-y Zaim, W. N. A. B. W. M., & Hamid, N. Z. A. (2017). Forecasting ozone pollutant (o3) in universiti pendidikan sultan idris, tanjung malim, perak, malaysia based on monsoon using chaotic approach. [Peramalan Bahan Pencemar Ozon (O3) di Universiti Pendidikan Sultan Idris, Tanjung Malim, Perak, Malaysia Mengikut Monsun dengan Menggunakan Pendekatan Kalut] Sains Malaysiana, 46(12), 2523-2528. doi:10.17576/jsm-2017-4612-30 |
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. |