Prediction Of Pm10 Concentrations Using Extreme Value Distributions (Evd) Classical And Bayesian Approaches

Keadaan zarahan tinggi (jerebu) secara umumnya dikaitkan dengan kehadiran PM10 atau PM2.5. Ia adalah penting untuk memaklumkan kepada umum terhadap tahap PM10 dan kepentingannya supaya langkah-langkah penyesuaian yang lebih berkesan dapat diambil bagi kalangan umum yang terjejas. Kajian ini dijalankan dengan objektif untuk membandingkan Taburan Nilai Melampau (EVD) menggunakan pendekatan konvensional dan Bayesian dan menggunakan taburan terbaik untuk peramalan kepekatan PM10 pada masa hadapan. Ketika ini, tiada pendekatan Bayesian di dalam kajian kepekatan PM10. Rekod daripada lapan stesen pengawasan di Semenanjung Malaysia telah dipilih untuk tempoh 1 Januari 2000 hingga 31 Disember 2012 selepas analisis awal untuk menilai kewujudan nilai melampau. Taburan dengan pengukuran ralat yang terkecil dan pengukuran kejituan tertinggi di lima stesen pemantauan Bukit Rambai, Jerantut, Nilai, Pasir Gudang dan Shah Alam adalah taburan menggunakan kaedah Bayesian dengan kebolehjadian GEV dan taburan prior tanpa maklumat menggunakan taburan seragam. Walau bagaimanapun, bagi Klang dan Seberang Jaya taburan EVD GEV disimpulkan sebagai taburan yang terbaik dan EVD dua parameter Weibull adalah taburan terbaik untuk Perai. Pendekatan Bayesian adalah lebih unggul dari kaedah konvensional apabila menggunakan data maksimum harian dan boleh digunakan untuk menilai tahap kepekatan tinggi PM10 untuk penggubal dasar melaksanakan dasar-dasar yang lebih berkesan untuk mewujudkan persekitaran yang lebih bersih. ________________________________________________________________________________________________________________________ High particulate event (haze) is generally associated with presence of PM10 or PM2.5. It is important to make known to public of PM10 level and its importance for more effective adaptation measures among the affected public. This study was conducted with the objectives to compare the best Extreme Value Distributions (EVD) using the conventional and Bayesian approaches and use the best distribution for the prediction of future PM10 exceedances. Currently, there is none on the application of Bayesian approach in the study of PM10 concentrations. Records from eight monitoring stations in the Peninsular Malaysia were selected for the period of 1st January 2000 to 31st December 2012 after preliminary analysis to check for the existance of extreme values. The distribution with the smallest error measures and highest accuracy measures in five of the monitoring stations Bukit Rambai, Jerantut, Nilai, Pasir Gudang and Shah Alam was the Bayesian GEV likelihood with uniform non-informative prior distribution. However, for Klang and Seberang Jaya the EVD GEV distribution was concluded as the best distribution and EVD two-parameter Weibull was the best distribution for Perai. The Bayesian approach is superior than the conventional method using the daily maximum data and can be used to assess high level of PM10 concentrations for the policy makers to implement effective policies to create cleaner environment

PM10 analysis for three industrialized areas using extreme value

One of the concerns of the air pollution studies is to compute the concentrations of one or more pollutants’ species in space and time in relation to the independent variables, for instance emissions into the atmosphere, meteorological factors and parameters. One of the most significant statistical disciplines developed for the applied sciences and many other disciplines for the last few decades is the extreme value theory (EVT). This study assesses the use of extreme value distributions of the two-parameter Gumbel, two and three-parameter Weibull, Generalized Extreme Value (GEV) and two and three-parameter Generalized Pareto Distribution (GPD) on the maximum concentration of daily PM10 data recorded in the year 2010 - 2012 in Pasir Gudang, Johor; Bukit Rambai, Melaka; and Nilai, Negeri Sembilan. Parameters for all distributions are estimated using the Method of Moments (MOM) and Maximum Likelihood Estimator (MLE). Six performance indicators namely; the accuracy measures which include predictive accuracy (PA), coefficient of determination (R2), Index of Agreement (IA) and error measures that consist of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Normalized Absolute Error (NAE) are used to find the goodness-of-fit of the distribution. The best distribution is selected based on the highest accuracy measures and the smallest error measures. The results showed that the GEV is the best fit for daily maximum concentration for PM10 for all monitoring stations. The analysis also demonstrates that the estimated numbers of days in which the concentration of PM10 exceeded the Malaysian Ambient Air Quality Guidelines (MAAQG) of 150 mg/m3 are between ½ and 1½ days

Tahap pembudayaan ICT (Information & Communication Technology) di kalangan pensyarah UiTM Pulau Pinang dan UiTM Perlis / Koni Md Taha, Tengku Muhaini Tuan Mat and Hasfazilah Ahmat

Teknologi maklumat dan komunikasi (ICT) merupakan wadah baru masyarakat hari ini dimana semua insan terpaksa kenal dan tahu memanfaatkannya. Adalah menjadi kewajipan untuk setiap tenaga pengajar di universiti untuk membudayakan penggunaan ICT supaya budaya ICT itu akan menjadi komponen lazim dalam kehidupan bagi melahirkan masyarakat yang bermaklumat dan berfikiran secara global. Objektif kajian ini adalah untuk mengenal pasti tahap pembudayaan ICT di kalangan pensyarah UiTM Pulau Pinang dan UiTM Perlis, persepsi pensyarah mengenai penggunaan ICT, tahap pengetahuan dan kemahiran ICT pensyarah, kekerapan penggunaan peralatan ICT serta halangan kepada penggunaan ICT. Kajian ini juga cuba mengenal pasti sama ada terdapat hubungan di antara tahap pembudayaan ICT dengan tahap pengetahuan dan kemahiran ICT pensyarah dan hubungan di antara tahap pembudayaan ICT dengan halangan kepada penggunaan ICT. Data dikumpulkan menggunakan borang soal selidik dan dianalisis menggunakan nilai kekerapan, peratusan, min, ujian-t dan ujian Chi-Square. Dapatan kajian menunjukkan tahap pembudayaan ICT di kalangan pensyarah di UiTM Pulau Pinang dan UiTM Perlis masih di tahap sederhana. Ini dikenal pasti berpunca daripada tahap pengetahuan dan kemahiran ICT pensyarah yang sederhana, kekurangan alat bantu mengajar, kursus dan bengkel ICT yang tidak mencukupi dan kurangnya sokongan teknikal untuk membudayakan penggunaan ICT dalam pengajaran dan pembelajaran

Enhancing attendance and student exam score based on mobile attendance application / Nurhafizah Ahmad... [et al.]

In today’s digital era, it is possible to use the latest technology to improve student attendance and performance. The purpose of the present study is to determine the relationship between absenteeism and academic performance among Calculus students, as well as to measure the impact of class absence on the student’s final exam scores. Based on this, the use of appropriate strategy was employed, which is the mobile attendance application to reduce absenteeism among students in higher educational institution. The selection of sample was based on cluster sampling, involving the selection of 87 repeater students. The data collected were analyzed using quartile regression and independent sample t-test. The result of the findings revealed that the class absence has an impact on the student’s final exam scores. This is because, if the student was absences by 1 class, the final exam score is expected to decrease on average by 1.89%. Hence, findings show that the percentage of absences for the students with manual attendance was higher than the percentage of absences for the students with mobile attendance application. The application can help to reduce absenteeism by reminding students about recent attendance records

Identification of Source Contributions to Air Pollution in Penang Using Factor Analysis

Penang is one of the rapidly developed states in Malaysia with large numbers of population industrial activities, motor vehicles density and development projects.  The concentrations of air pollution parameters in Penang were investigated and analyzed together with meteorological parameters in order to determine their characteristics and contributions to air pollution in Penang using factor analysis (FA).  The air pollution parameters include ground level ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2) and particulate matters of less than 10 microns in size (PM10) while the meteorological parameters include relative humidity, wind speed and temperature.  The data was obtained from the Department of Environment (DOE) for the Universiti Sains Malaysia (USM) monitoring station for the period of 10 years from 2004 to 2013.  In this study, concentrations of PM10 was found to be the highest among the air pollutants and the concentrations was at its highest between the months of June to September for almost all years of observation due to the southwest monsoon.  As for the source contributions of air pollutions, O3 and meteorological parameters were found to be the largest contributor to air pollutions in Penang, followed by the traffic emissions and industrial activities

THE MALAYSIA PM10 ANALYSIS USING EXTREME VALUE

The study of air quality is closely associated to air pollution. Air pollution is of the main concerns of the authority in view of the fact that it can generate damaging effects to human health, crops and environment. This paper assesses the use of Extreme Value Distributions (EVD) of the two-parameter Gumbel, two and three-parameter Weibull, Generalized Extreme Value (GEV) and two and three-parameter Generalized Pareto Distribution (GPD) on the maximum concentration of daily PM10 data recorded in the year 2010 - 2012 in Pasir Gudang, Johor, Bukit Rambai, Melaka and Nilai, Negeri Sembilan. Parameters for all distributions were estimated using the method of Maximum Likelihood Estimator (MLE). The goodness-of-fit of the distribution was determined using six performance indicators namely; the accuracy measures which include Prediction Accuracy (PA), Coefficient of Determination (R2), Index of Agreement (IA) and error measures that consist of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Normalized Absolute Error (NAE). The best distribution was selected based on the highest accuracy measures which are close to 1 and the smallest error measures. The result showed that the Generalized Extreme Value (GEV) distribution was the best fit for daily maximum concentration for PM10 for all monitoring stations. The GEV gave the smallest errors (NAE, RMSE and MAE) and the highest accuracy measures (PA, R2 and IA) when compared to other distributions. The method gave the accuracy of more than 98% in PA, IA and R2 for all stations. The analysis demonstrated that the estimated numbers of days in which the concentration of PM10 exceeded the Malaysian Ambient Air Quality Guidelines (MAAQG) of 150 µg/m3 were between ½ and 2 days

PM10 Analysis for Three Industrialized Areas using Extreme Value

One of the concerns of the air pollution studies is to compute the concentrations of one or more pollutants’ species in space and time in relation to the independent variables, for instance emissions into the atmosphere, meteorological factors and parameters. One of the most significant statistical disciplines developed for the applied sciences and many other disciplines for the last few decades is the extreme value theory (EVT). This study assesses the use of extreme value distributions of the two-parameter Gumbel, two and three-parameter Weibull, Generalized Extreme Value (GEV) and two and three-parameter Generalized Pareto Distribution (GPD) on the maximum concentration of daily PM10 data recorded in the year 2010 - 2012 in Pasir Gudang, Johor; Bukit Rambai, Melaka; and Nilai, Negeri Sembilan. Parameters for all distributions are estimated using the Method of Moments (MOM) and Maximum Likelihood Estimator (MLE). Six performance indicators namely; the accuracy measures which include predictive accuracy (PA), coefficient of determination (R2), Index of Agreement (IA) and error measures that consist of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Normalized Absolute Error (NAE) are used to find the goodness-of-fit of the distribution. The best distribution is selected based on the highest accuracy measures and the smallest error measures. The results showed that the GEV is the best fit for daily maximum concentration for PM10 for all monitoring stations. The analysis also demonstrates that the estimated numbers of days in which the concentration of PM10 exceeded the Malaysian Ambient Air Quality Guidelines (MAAQG) of 150 mg/m3 are between ½ and 1½ days

Characteristic and Prediction of Carbon Monoxide Concentration using Time Series Analysis in Selected Urban Area in Malaysia

Carbon monoxide (CO) is a poisonous, colorless, odourless and tasteless gas. The main source of carbon monoxide is from motor vehicles and carbon monoxide levels in residential areas closely reflect the traffic density. Prediction of carbon monoxide is important to give an early warning to sufferer of respiratory problems and also can help the related authorities to be more prepared to prevent and take suitable action to overcome the problem. This research was carried out using secondary data from Department of Environment Malaysia from 2013 to 2014. The main objectives of this research is to understand the characteristic of CO concentration and also to find the most suitable time series model to predict the CO concentration in Bachang, Melaka and Kuala Terengganu. Based on the lowest AIC value and several error measure, the results show that ARMA (1,1) is the most appropriate model to predict CO concentration level in Bachang, Melaka while ARMA (1,2) is the most suitable model with smallest error to predict the CO concentration level for residential area in Kuala Terengganu

Characteristic and Prediction of Carbon Monoxide Concentration using Time Series Analysis in Selected Urban Area in Malaysia

Carbon monoxide (CO) is a poisonous, colorless, odourless and tasteless gas. The main source of carbon monoxide is from motor vehicles and carbon monoxide levels in residential areas closely reflect the traffic density. Prediction of carbon monoxide is important to give an early warning to sufferer of respiratory problems and also can help the related authorities to be more prepared to prevent and take suitable action to overcome the problem. This research was carried out using secondary data from Department of Environment Malaysia from 2013 to 2014. The main objectives of this research is to understand the characteristic of CO concentration and also to find the most suitable time series model to predict the CO concentration in Bachang, Melaka and Kuala Terengganu. Based on the lowest AIC value and several error measure, the results show that ARMA (1,1) is the most appropriate model to predict CO concentration level in Bachang, Melaka while ARMA (1,2) is the most suitable model with smallest error to predict the CO concentration level for residential area in Kuala Terengganu

A Novel Hybrid Model Combining the Support Vector Machine (SVM) and Boosted Regression Trees (BRT) Technique in Predicting PM₁₀ Concentration

The PM10 concentration is subject to significant changes brought on by both gaseous and meteorological variables. The aim of this research was to explore the performance of a hybrid model combining the support vector machine (SVM) and the boosted regression trees (BRT) technique in predicting the PM10 concentration for 3 consecutive days. The BRT model was trained by utilizing maximum daily data in the cities of Alor Setar, Klang, and Kuching from the years 2002 to 2017. The SVM–BRT model can optimize the number of predictors and predict PM10 concentration; it was shown to be capable of predicting air pollution based on the models’ performance with NAE (0.15–0.33), RMSE (10.46–32.60), R2 (0.33–0.70), IA (0.59–0.91), and PA (0.50–0.84). This was accomplished while saving training time by reducing the feature size given in the data representation and preventing learning from noise (overfitting) to improve accuracy. This knowledge establishes the foundation for the development of efficient methods to prevent and/or minimize the health effects of PM10 exposure on one’s health