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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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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Laboratories' air quality has an impact on employees' and students’ comfort and health. Particulate matter can be regarded as one of the most important and frequently encountered indoor air pollutants. This study aims to measure the concentrations of PM2.5, total bacterial counts (TBC) and total fungal counts (TFC), as well as the morphological structure of PM2.5 in selected laboratories at Level 2 and 3, Block 2, Faculty of Science and Mathematics, Sultan Azlan Shah Campus, UPSI. The data collection took place in three different laboratories. During the 8-hour sampling session, samples of PM2.5 were collected using a low-volume air sampler (LVS). In addition, airborne microorganisms were collected using a microbial sampler. The morphological structure of PM2.5 was also observed using the Field-Emission Scanning Electron Microscope (FESEM). Results revealed the average concentration of PM2.5 of 0.56 ± 0.24 μgm-3, with Lab A (biochemistry laboratory) exhibited the highest concentrat.....

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Predicting the occurrence of haze is of great importance due to its negative impact on human health, the environment, and the economy. This study aims to develop a model for predicting haze using chaos theory. The data were taken from an industrial area, Klang, Selangor Malaysia during Southwest Monsoon. The model is trained using historical data on haze occurrences and the accuracy of the prediction is evaluated using a testing dataset. A chaos model, namely local mean approximation method (LMAM) will be used to predict the haze phenomenon. Results show that the chaos-based approach is effective in forecasting the onset and duration of haze events. The predicting model can provide early warnings for policymakers and relevant authorities, enabling them to take proactive measures to mitigate the effects of haze on public health and the environment. The model also presents a promising alternative to traditional forecasting techniques and highlights the potential applications of chaos the.....

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Forecasting haze is essential for protecting the environment, the economy, and public health. It assists authorities in taking preventative action to lessen the adverse effects of haze episodes and boost community resistance to air pollution. The goal of this study was to create a model for haze prediction by using two methods, multiple linear regression and chaos theory. In this study, chaos theory forecasts haze using univariate time series which is PM10, whereas multiple linear regression (MLR) utilizes multivariate time series for its predictions, namely ambient temperature, wind speed, ozone, nitrogen dioxide, carbon monoxide, and sulphur dioxide. Data for this study will be collected during the southwest monsoon from an industrial area in Klang, Selangor. The results of these two models will be compared to determine which model gave better performance. With these predictive models, policymakers and relevant authorities can receive timely alerts, allowing them to implement prevent.....

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