Kajian ini adalah aplikasi pendekatan kalut ke atas pemodelan siri masa bahan pencemar udara ozon (03), zarah terampai (PMIO) dan jerebu yang dicerap mengikut jam di stesen-stesen asas dan metropolitan di Malaysia. Pemodelan kalut melibatkan dua peringkat iaitu (i) analisa dinamik siri masa dan (ii) pembinaan model peramalan. Peringkat (i) melalui kaedah Cao, kaedah m-songsang dan plot ruang fasa menunjukkan kehadiran dinamik kalut dalam setiap siri masa. Peringkat (ii) melibatkan dua langkah iaitu (a) pembinaan semula ruang fasa dan (b) proses peramalan. Untuk (a), dua parameter diperlukan iaitu masa tunda t dan matra pembenaman m . Parameter t: ditentukan melalui penetapan t = 1, kaedah purata maklumat bersama dan kaedah baharu t: - songsang. Parameter m dikira melalui kaedah Cao dan kaedah m-songsang. Langkah (b) dijalankan melalui kaedah penghampiran purata setempat (kpps), kaedah penghampiran linear setempat (kpls) dan kaedah penambahbaikan penghampiran linear setempat (kppls). Pe.....

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This paper is about modeling of chaotic systems via nearest neighbor approach. This approach holds the principle that future data can be predicted using past data information. Here, all the past data known as neighbors. There are various prediction models that have been developed through this approach. In this paper, the zeroth-order approximation method (ZOAM) and improved ZOAM, namely the k-nearest neighbor approximation (KNNAM) and weighted distance approximation method (WDAM) were used. In ZOAM, only one nearest neighbor is used to predict future data while KNNAM uses more than one nearest neighbor and WDAM add the distance element for prediction process. These models were used to predict one of the chaotic data, Logistic map. 3008 Logistic map data has been produced, in which the first 3000 data were used to train the model while the rest is used to test the performance of the model. Correlation coefficient and average absolute error are used to view the performance of the model. .....

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This study focuses on the analysis and prediction of hourly ozone (O3) pollution in one of Malaysian urban area namely Shah Alam through chaotic approach. This approach begins by detecting the chaotic behavior of the O3 pollution using phase space plot and Cao method. Then, the local mean approximation method is used for prediction purposes. The O3 pollution observed at Shah Alam is detected as chaotic in behavior. Due to the chaotic behavior, only short-term prediction is allowed. Thus, the one-hour ahead prediction is done through the local mean approximation method. The prediction result shows that correlation coefficient value between the observed and predicted time series is near to one. This excellent prediction result shows in particular that the local mean approximation method can be used to predict the O3 pollution in urban area. In general, chaotic approach is a useful approach that can be used to analyze and predict the O3 pollution time series...

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Forecasting and analyzing the ozone (O3) concentration time series is important because the pollutant is harmful to health. This study is a pilot study for forecasting and analyzing the O3 time series in one of Malaysian educational area namely Shah Alam using chaotic approach. Through this approach, the observed hourly scalar time series is reconstructed into a multi-dimensional phase space, which is then used to forecast the future time series through the local linear approximation method. The main purpose is to forecast the high O3 concentrations. The original method performed poorly but the improved method addressed the weakness thereby enabling the high concentrations to be successfully forecast. The correlation coefficient between the observed and forecasted time series through the improved method is 0.9159 and both the mean absolute error and root mean squared error are low. Thus, the improved method is advantageous. The time series analysis by means of the phase space plot and .....

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Analisis dan peramalan siri masa suhu adalah penting kerana perubahan suhu boleh membawa kesan serius kepada kesihatan. Kajian ini dijalankan bertujuan menganalisis dan meramal siri masa suhu di Jerantut, Pahang, Malaysia dengan menggunakan pendekatan kalut. Pemodelan kalut dibahagikan kepada dua tahap; pembinaan semula ruang fasa dan proses peramalan. Melalui pembinaan semula ruang fasa, data skalar satu matra dibina semula menjadi ruang fasa multimatra. Ruang fasa multimatra ini digunakan untuk mengesan kehadiran dinamik kalut melalui kaedah plot ruang fasa dan kaedah Cao. Keputusan menunjukkan bahawa siri masa yang diperhatikan bersifat kalut. Oleh itu, peramalan satu jam ke hadapan dibina melalui kaedah penghampiran purata setempat yang merupakan kaedah peramalan asas menggunakan pendekatan kalut. Nilai pekali korelasi yang diperoleh adalah 0.9443. Nilai yang menghampiri satu ini menunjukkan hasil peramalan yang bagus dengan merupakan refleksi bahawa siri masa yang diramal dan siri.....

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This study is designed to i) apply chaotic approach in predicting Carbon Monoxide (CO) data series and ii) improve the method in determining number of k–nearest neighbor. Chaotic approach is one alternative approach to predict any data series. Prediction through chaotic approach is made after three important parameters which are delay time τ, embedding dimension m and numbers of nearest neighbor k were determined. Therefore, the chaotic approach is applied. In this study, predictions are done to Carbon Monoxide time series observed at Shah Alam in Malaysia. Parameters τ and m are determined through average mutual information and Cao method respectively. While for k, most of the past researches frequently used try and error method. In this study an improvement of the method in determining the number of k is introduced. This improved method is done through plotting the graph of k versus the correlation coefficient (cc) of prediction model. Parameter cc is obtained through the predict.....

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This study focused on development of PM10 pollutant forecasting model at different geographical areas namely background (Jerantut, Pahang), industrial (Bukit Rambai, Malacca) and urban (Klang, Selangor) through phase space reconstruction approach. Firstly, PM10 pollutant is reconstructed into a multi-dimensional phase space. Then, the reconstruct phase space is used to forecast future PM10 pollutant. Comparison with traditional approach of autoregressive linear through mean absolute error and root mean squared error showed that phase space reconstruction approach is better. Furthermore, values of correlation coefficient showed that PM10 pollutant is forecasted well through phase space reconstruction approach. In conclusion, development of PM10 pollutant forecasting models at different geographical areas are success. These findings are expected to help stakeholders in having a better PM10 pollutant management...

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The aim of this study is to model the temperature time series at Malaysian high population area during dry season through chaos theory. The selected high population area is Shah Alam located in Selangor state of Malaysia. Chaos theory modelling is categorized into two parts namely analysis and prediction. Analysis by the phase space plot showed that the nature of the observed temperature time series is chaos. Hence, the time series is predicted via the chaotic model. Results from the chaotic model showed that the temperature time series is well predicted with Pearson correlation coefficient near to 1. The result is compared with the traditional method of autoregressive linear model. Based on the computed values of average absolute error, root mean squared error and Pearson correlation coefficient, the chaotic model is found better in predicting temperature time series at Shah Alam area during dry season. This indicates that the chaos theory is applicable for temperature time series at .....

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