Analysis and prediction of temperature time series is important because temperature changes can affect human’s health. The objectives of this study are to analyse and predict the temperature series in Jerantut, Pahang, Malaysia using chaotic approach. Modelling through chaotic approach divided into two stages; reconstruction of phase space and prediction processes. Through the reconstruction of phase space, a single scalar time series is rebuilt into a multi-dimensional phase space. This multi-dimensional phase space is used to detect the presence of chaotic dynamics through phase space plot and Cao method. The results show that the observed time series is chaotic in dynamic. Therefore, one hour ahead prediction through local mean approximation method is done. The correlation coefficient value obtained is 0.9789. The value which is approaching one reflected that the predicted time series and observed time series are close to each other. Thus, the modelling through chaotic approach is considered succeed. It is hoped that the model can help Malaysian Meteorological Department and Department of Environment Malaysia in order to improve their weather services.

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.

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ABSTRAK Kajian ini merupakan aplikasi pendekatan kalut ke atas peramalan siri masa bahan pencemar udara ozon di stesen asas Malaysia yang terletak di (2016) melaporkan bahawa pencemaran ozon meningkatkan kadar kematian kerana ia membawa kepada pelbagai penyakit pernafasan dan kardiovaskular. Oleh itu, pembangunan model peramalan ke atas siri masa bahan pencemar udara ozon adalah penting.Sifat sesebuah siri masa boleh dikelaskan kepada berketentuan atau rawak. Siri masa berketentuan adalah siri masa yang boleh diramal manakala siri masa rawak tidak boleh diramal. Sifat kalut berada antara sifat berketentuan dan rawak (Abarbanel 1996). Siri masa kalut boleh diramal; walau bagaimanapun, disebabkan oleh kebergantungan sensitif kepada keadaan awal, maka, bagi sesebuah siri masa kalut, hanya peramalan jangka pendek dibenarkan (Sprott 2003).Terdapat pelbagai pendekatan yang telah digunakan oleh kajian lepas untuk menguji sama ada siri masa ozon adalah kalut atau tidak. Menggunakan kaedah matra korelasi, kamiran kolerasi, entropi dan eksponen Lyapunov, kajian seperti Chattopadhyay dan Chattopadhyay Plot ruang fasa dan kaedah Cao (Cao 1997) dapat mengklasifikasikan sifat siri masa. Walau bagaimanapun, kaedah ini jarang digunakan ke atas siri masa ozon walaupun kedua-dua telah terbukti berkesan oleh kajian

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