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.
      1  6
  • 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.
      1  13