Civil Engineering and Architecture 9(2): 493-499, 2021 http://www.hrpub.org DOI: 10.13189/cea.2021.090221 Water Level Prediction Using Different Numbers of Time Series Data Based on Chaos Approach Adib Mashuri 1 , Nur Hamiza Adenan 2,* , Nor Suriya Abd Karim 2 , Nor Zila Abd Hamid 2 1 Department of General Studies, Batu Lanchang Vocational College,11600 Jelutong, Pulau Pinang, Malaysia 2 Department of Mathematics, Faculty Science and Mathematics, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia Received January 7, 2021; Revised February 26, 2021; Accepted March 12, 2021 Cite This Paper in the following Citation Styles (a): [1] Adib Mashuri, Nur Hamiza Adenan, Nor Suriya Abd Karim, Nor Zila Abd Hamid , "Water Level Prediction Using Different Numbers of Time Series Data Based on Chaos Approach," Civil Engineering and Architecture, Vol. 9, No. 2, pp. 493-499, 2021. DOI: 10.13189/cea.2021.090221. (b): Adib Mashuri, Nur Hamiza Adenan, Nor Suriya Abd Karim, Nor Zila Abd Hamid (2021). Water Level Prediction Using Different Numbers of Time Series Data Based on Chaos Approach. Civil Engineering and Architecture, 9(2), 493-499. DOI: 10.13189/cea.2021.090221. Copyright©2021 by authors, all rights reserved. Authors agree that this article remains permanently open access under the terms of the Creative Commons Attribution License 4.0 International License Abstract 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. Keywords Amount of Data, Prediction, Chaos Approach, Water Level 1. Introduction Cao, Tao, Dong and Li [5] asserted that floods occur when excessive water level rises in river areas, whether in natural or man-made conditions. From a scientific vocabulary point of view, floods are caused by the existence of excessive heavy rain that cannot be supported by the river basin and thus the water overflows to the riverbanks or floodplains [18]. Flood disaster can cause damage to people and nature where it can affect the land structure, agriculture, livestock as well as residential areas [7]. Therefore, prediction of water level in floodplain area is important for early signals and flood control. The dynamics of a time series data can be divided into two parts; deterministic and random. In 1963, Lorenz [15] discovered the dynamic chaotic where the knowledge was used for research in the field of science and engineering in a thorough matter while the term chaos was first introduced by Li and Yorke [14]. According to Abarbanel [1], chaotic dynamic is between the deterministic and random dynamic. Chaotic time series can be used in prediction only in a short term due to the sensitive dependence on initial conditions [19]. This chaos approach is an important discovery for predicting phenomena in scientific research. The application of chaos approach is widely used in many types of time series data such as river flow [3], ozone [9] and sea level [4]. Nowadays, the research in the application of this method on water level time series data is growing and being conducted in several countries such as in China [10], Iran [21] and Malaysia [16]. In addition, a lot of research also emphasised on time scale of water level data such as hourly scale [13], daily [12] and weekly