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 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 on the traffic flow time series data besides give excellent prediction with the value of correlation coefficient more than 0.7500. Hence, inverse approach of chaos theory can develop to prediction model towards the traffic flow in urban area; thus may help the local authorities to provide good traffic management.
Aras air yang agak tinggi, tidak menentu dan melebihi tebing sungai adalah penyebab kepada bencana banjir. Ini memberi kesan kepada berlakunya banjir di kawasan pinggir sungai akibat daripada paras air yang tidak menentu. Kajian ini menggunakan data siri masa di Sungai Dungun, Terengganu bermula daripada April 2009 hingga Mei 2010 melibatkan bacaan paras air yang melebihi paras bahaya. Tujuan kajian ini adalah untuk mengesan kehadiran telatah kalut dan dan seterusnya membuat peramalan aras air sungai di Sungai Dungun. Pengesanan kehadiran telatah kalut adalah dengan menggunakan kaedah plot ruang fasa dan kaedah Cao. Manakala, peramalan aras air dilakukan menggunakan kaedah penambahbaikan kaedah peramalan purata setempat (penambahbaikan KPPS). Hasil kajian menunjukkan telatah kalut hadir dengan menggunakan kaedah plot ruang fasa dan kaedah Cao. Hasil peramalan menunjukkan bahawa kaedah penambahbaikan ini dapat memberikan hasil peramalan yang cemerlang dengan nilai pekali korelasi melebihi 0.999000. Perbandingan hasil peramalan turut dilaksanakan dengan menggunakan kaedah peramalan purata setempat (KPPS) pada data yang sama. Hasil perbandingan ketepatan peramalan menunjukkan bahawa kaedah penambahbaikan KPPS adalah lebih tepat berbanding peramalan menggunakan kaedah KPPS dengan peningkatan ketepatan hasil peramalan sebanyak 1.77%. Oleh itu, kaedah penambahbaikan KPPS ini adalah sesuai dan dicadangkan untuk digunakan dalam meramal data siri masa aras air sungai di kawasan banjir dan seterusnya memberi manfaat kepada pihak berkuasa tempatan yang bertanggungjawab bagi memberikan amaran awal bencana banjir di kawasan terlibat.
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