Determining the Chaotic Dynamics of Hydrological Data in Flood-Prone Area

Abstract

Flood-prone areas are associated with hydrological time series data such as rainfall, water level and river flow. The possibility to predict flood is to relate all the three data involved. However, in order to develop a multivariable prediction model based on chaos approach, each datum needs to identify chaotic dynamics. As such, the Sungai Galas, Dabong in Kelantan, Malaysia which is a flood disaster area has been selected for the analysis. Rainfall, water level and river flow data in this area were collected to be analysed using the Cao method to identify the presence of chaotic dynamics. The hydrological data is uncertain, which is difficult to predict because the data involved is located in the area of flood disaster. The analysis showed the presence of chaotic dynamics on rainfall, water level and river flow data in the Sungai Galas which involved uncertain data located in flood affected areas by using Cao method. Therefore, a multivariable flood prediction model can be implemented using a chaos approach.

Traffic Flow Prediction in Urban Area Using Inverse Approach of Chaos Theory

“…The main reason for detecting chaotic behaviour is to develop short term prediction model [18]. There are several methods used to determine the existence of chaotic behaviour on various time series data such as phase space plot [19], correlation dimension [20] and Cao method [10]. However, this study used phase space plot in order to determine the existence of chaotic behaviour in traffic flow time series data that were observed hourly.…”

“…The application of chaos approach in modelling the prediction of traffic flow time series data in Malaysia is still in the early stage. Until today, chaos approach has been applied to meteorology and hydrology areas in predicting ozone concentration and river flow in Malaysia [10][11][12] but no study on traffic management using chaos approach has been conducted.…”

Chaotic Identification of Hourly and Daily Water Level Time Series Data in Different Areas of Elevation at Pahang River

“…There are various methods used in determining chaotic dynamics for hydrological time series data such as Cao method (Mashuri et al, 2019), phase space plot (Yildirim, Hacinliyan, Akkaya, & Ikiel, 2016), correlation dimension (Albostan & Önöz, 2015) and Lyapunov exponent (Mihailović et al, 2019). Each method gives different result in analysing the existence of chaotic dynamics (Khatibi et al, 2012) In this study, Cao method and phase space plots were used to identify the chaotic dynamics.…”

“…Chaotic dynamics identification using time series was first discovered by (Lorenz, 1963). Since 1963, the study on this approach has been widely investigated in various field such as in atmospheric (Ruslan & Hamid, 2019) and hydrology (Mashuri, Adenan, & Hamid, 2019). As the world is changing into a modern world, vast knowledge and applications on chaos approach have been developed including the implementation of chaos in science and engineering as well as in hydrology area.…”

Water Level Prediction Using Different Numbers of Time Series Data Based on Chaos Approach

“…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 [12].…”