Aliran sungai penting untuk dianalisis dan diramal bagi menyalurkan maklumat aliran sungai di kawasan berkepentingan di Malaysia bagi tujuan pengurusan sumber air dan pengawalan banjir. Maka, satu kajian telah dijalankan dengan mengaplikasikan pendekatan kalut bagi menganalisis dan meramal aliran sungai di beberapa kawasan berkepentingan di Malaysia. Perlaksanaan kajian ini adalah merangkumi tiga objektif iaitu (1) mengenal pasti kehadiran telatah kalut pada aliran sungai; (2) meramal siri masa aliran sungai dan (3) mencadangkan kaedah penambahbaikan bagi peramalan aliran sungai. Kajian ini diperincikan kepada kawasan yang lebih spesifik bagi meneliti kesesuaian kaedah yang digunakan. Kawasan yang berbeza ini adalah meliputi stesen aliran sungai di kawasan yang mempunyai keluasan tadahan air yang berbeza, aliran sungai di dataran banjir dan aliran sungai di bandar. Kaedah dimensi korelasi digunakan bagi mencapai objektif yang pertama. Hasil kajian menunjukkan semua aliran sungai yang d.....

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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 .....

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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 implemente.....

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Traffic flow is a continuous phenomenon. The irregular patterns in the traffic flow data show the complexity of the system under a variety of internal and external factors of restrict and influence. As such, local mean prediction method (LMPM) has been employed in order to do prediction. Firstly, traffic flow data has been reconstructed to m-dimensional phase space. Next, the phase space is used to predict traffic flow using chaos approach. The traffic flow data reveals chaotic behaviour through analysis using Cao method. The prediction performance shows that the value of correlation coefficient (CC) is greater than 0.5 for all stations that have been analyzed. The value of CC is good and acceptable due to the complexity of the system. These findings are expected to assist in fulfilling the Road Safety Plan 2014-2020 by the Ministry of Transportation as well as local authorities in traffic management...

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Traffic flow is a continuous phenomenon. The irregular patterns in the traffic flow data show the complexity of the system under a variety of internal and external factors restrict and influence. The possibility of making short range forecasting of traffic flow using chaos approach is by investigating the presence of chaotic behaviour. Traffic flow data of four stations located in Selangor, Malaysia were analysed. There were three methods employed in this analysis; (1) phase space plot, (2) Cao method and (3) Lyapunov exponent. The phase space plot can be constructed by phase space. There were two parameters needed in phase space reconstructed; (1) time delay, ? that is calculated by using average mutual information (AMI) and (2) embedding dimension, m that is obtained from Cao method. The traffic flow data were analysed to reveal the existence of chaotic behaviour. Therefore, short range forecasting of traffic flow using chaos approach can be applied to show the suitability of chaos a.....

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Traffic congestions problem could affect everyday life especially in urban area. In order to solve the issue, an excellent traffic flow prediction needs to be developed for a better traffic management. Hence, this study was conducted in order to predict traffic flow by using the data of total volume of vehicles per hour at two main roads located in urban areas namely Selangor and Kuala Lumpur, Malaysia by using application of chaos theory. Phase space reconstruction was used to determine the chaotic behaviour of the total volume of vehicles per hour data. The reconstruction of phase space involves a single variable of the total volume of vehicles per hour data to m-dimensional phase space. Meanwhile, the inverse approach as well as local linear approximation method was used to develop prediction model of the traffic flow time series data. This study found that (i) the time series data were chaotic behaviour based on the phase space plot and (ii) inverse approach can provide prediction .....

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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 combi.....

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