Conference Publications

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https://hdl.handle.net/20.500.14170/199

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Browsing Conference Publications by Author "Ahmad Zia Ul-Saufie"
  • Publication
    A review of PM₁₀ concentrations modelling in Malaysia
    (IOP Publishing Ltd., 2020)
    Wan Nur Shaziayani
    ;
    Ahmad Zia Ul-Saufie
    ;
    Zuraira Libasin
    ;
    Fuziatul Norsyiha Ahmad Shukri
    ;
    Sharifah Sarimah Syed Abdullah
    ;
    Norazian Mohamed Noor
    The purpose of predictive modelling is to predict the variable of interest with reasonable precision, and often to assess the contribution of the independent variables to the dependent variable. In this paper, all of the works examined are aimed at predicting concentrations of outdoor PM₁₀ concentrations. The vast majority of the works reported used almost exclusively predictors of the meteorological and source emissions. However, the use of the Hybrid model in predicting PM₁₀ concentrations is still not widely used in Malaysia.
      2  10
  • Publication
    Comparative analysis of machine learning techniques for SO₂ prediction modelling
    (IOP Publishing, 2023)
    Wan Nur Shaziayani
    ;
    Norazian Mohamed Noor
    ;
    Muhamad Azan Yaakob
    ;
    Ahmad Zia Ul-Saufie
    Sulphur dioxide (SO₂) is produced both naturally and by human activity. The primary natural resource is derived from volcanoes. The burning of fossil fuels is the primary anthropogenic source (especially coal and diesel). Therefore, a reliable and accurate predicting method is essential for an early warning system for SO₂ atmospheric concentration. There are still limited studies in Malaysia that use machine learning methods to predict SO₂ concentrations. With the aid of machine learning, this study seeks to develop and predict future SO₂ concentrations for the next day using the maximum daily data from Klang, Selangor. RapidMiner Studio is the data mining tool used for this research work. Based on the results, it showed that the SVM model was the best guide to be used compared with the other five models (GLM, DL, DT, GBT, and RF). The performance indicators showed that the SVM model was adequate for the next day's prediction (R2 = 0.77, SE = 8.26, REL = 18.69%, AE = 1.46, and RMSE = 2.82). The developed model in this research can be used by Malaysian authorities as a public health protection measure to give Malaysians an early warning about the problem of air pollution. The goal of predictive modelling is to make a reasonable prediction of the variable of interest, and frequently, to determine how much the independent variable contributed to the dependent variable. The results also showed that the previous SO₂ concentrations were one of the most influential parameters used to predict the future SO₂ concentrations.
  • Publication
    Modified linear regression for predicting ambient particulate pollutants (PM₁₀) during High Particulate Event
    (IOP Publishing, 2023)
    Izzati Amani Mohd Jafri
    ;
    Norazian Mohamed Noor
    ;
    Nur Alis Addiena A. Rahim
    ;
    Syaza Ezzati Baidrulhisham
    ;
    Norazrin Ramli
    ;
    Ahmad Zia Ul-Saufie
    ;
    György Deák Habil
    Particulate Matter (PM₁₀) is one of the most significant contributors towards haze or high particulate event (HPE) that occurs in Malaysia. HPE can severely affect human health, environment and economic so it is important to create a reliable prediction model in predicting future PM₁₀ concentration especially during HPE. Therefore, the aim of this study is to investigate the performance of modified linear regression models in predicting the next-day Particulate Matter (PM₁₀+24) concentration at two areas in the peninsular Malaysia namely, Bukit Rambai and Nilai. Hourly air quality dataset during historic HPE in 1997, 2005, 2013 and 2015 were used for analysis. Pearson correlation was used to select the input of the PM₁₀ prediction model where only parameters with moderate (0.6 > r > 0.3) and strong (r > 0.6) correlation with PM₁₀ concentration were selected as independent variables input in creating the multiple linear regression (MLR) model. The performance of modified linear regression model was evaluated by using several performance indicator which is Prediction Accuracy (PA), Index of Agreement (d 2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results show that the modified MLR (parameter with r > 0.6 included as input) gave the best prediction model for the next-day PM₁₀ concentration in both Bukit Rambai and Nilai.
  • Publication
    Prediction of missing data in rainfall dataset by using simple statistical method
    (IOP Publishing, 2020)
    Izzati Amani Mohd Jafri
    ;
    Norazian Mohamed Noor
    ;
    Ahmad Zia Ul-Saufie
    ;
    Annas Suwardi
    Almost all of the data obtained from hydrological station contains missing data. Usually, this problem occurs due to equipment failures, maintenance work and human error. Incomplete dataset will reduce the ability of a statistical analysis and can cause a bias estimation due to systematic differences between observed and unobserved data. In this study, four simple statistical method such as Series Mean, Average Mean Top Bottom, Linear Interpolation and Nearest Neighbour were applied to predict the missing values in a rainfall dataset. An annual daily data for rainfall from nine selected monitoring station (from 2009 until 2018) were described using descriptive statistic. Then, the dataset were randomly simulated into 4 percentages of missing (5%, 10%, 15% and 20%) by using statistical package for social sciences software. The performance of this imputation methods were evaluated by using four performance indicators namely Mean Absolute Error, Root Mean Squared Error, Prediction Accuracy, and Index of Agreement. Overall, Linear Interpolation method was selected as the best imputation method to predict the missing data in the rainfall dataset.
      2  15