Norazian Mohamed Noor
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Preferred name
Norazian Mohamed Noor
Official Name
Norazian, Mohamed Noor
Alternative Name
Noor, Norazian Mohamed
Norazian, M. N.
Noor, Norazian Mohamad
Noor, N. M.
Mohamed, Norazian N.
Mohamed, N. N.
Zizi, Na Mohd
Main Affiliation
Scopus Author ID
25221616600
Researcher ID
M-6956-2019

Research Output
Now showing 1 - 3 of 3
  • Publication
    Performance of Bayesian Model Averaging (BMA) for short-term prediction of PM10 concentration in the Peninsular Malaysia
    (MDPI, 2023)
    Norazrin Ramli
    ;
    Hazrul Abdul Hamid
    ;
    Ahmad Shukri Yahaya
    ;
    Ahmad Zia Ul-Saufie
    ;
    Norazian Mohamed Noor
    ;
    Ain Nihla Kamarudzaman
    ;
    György Deák
    ;
    Nor Amirah Abu Seman @ Haji Ahmad
    In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years’ worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models’ performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.
  • Publication
    The replacement of missing values of continous air pollution monitoring data using Mean Top Bottom Imputation technique
    ( 2006)
    Norazian Mohamed Noor
    ;
    Ahmad Shukri Yahaya
    ;
    Nor Azam Ramli
    ;
    Mohd. Mustafa Al Bakri Abdullah
    Air pollutants data such as PM10 carbon monoxide, sulphur dioxide and ozone concentration were obtained from automated monitoring stations. These data usually contain missing values that can cause bias due to systematic differents between observed and unobserved data. Therefore, it is impirtant to find the best way to estimate these missing values to ensure that the data analyzed are of high precision. This paper focuses on the usage of mean top bottom imputation technique to replace the missing values. Three performance indicators were calculated in order to describe the goodness of fit of this technique. In order to test the efficiency of the method applied, PM10 monitoring dataset for Kuala Lumpur was used as case study. Three distributions that are Weibull, gamma and lognormal were fitted to the datasets after replacement of missing values using mean top bottom method and performance indicators were calculated to describe the qualities of the distributions. The results show that mean top bottom method gives very good performances at low percentage of missing data but the performances slightly decreased at higher degree of complexity. It was found that gamma distribution is the most appropriate distribution representing PM10 emissions in Kuala Lumpur.
      4  10
  • Publication
    Short-term predictions of PM₁₀ using Bayesian Regression Models
    (EDP Sciences, 2023)
    Norazrin Ramli
    ;
    Hazrul Abdul Hamid
    ;
    Ahmad Shukri Yahaya
    ;
    Norazian Mohamed Noor
    ;
    Holban Elena
    One of the air pollutants that poses the greatest threat to human health is PM10. The objectives of this study are to develop a prediction model for PM10. The Multiple Linear Regression (MLR) and Bayesian Regression (BRM) models were constructed to forecast the following dayâ s (Day 1) and next two daysâ (Day 2) PM10 concentration. To choose the optimal model, the performance metrics (NAE, RMSE, PA, IA, and R2) are applied to each model. Jerantut, Nilai, Shah Alam, and Klang were chosen as monitoring sites. Data from the Department of Environment Malaysia (DOE) was utilised as a case study for five years, with seven parameters (PM10, temperature, relative humidity, NO2, SO2, CO, and O3) chosen. According to the findings, the key factors responsible for the unhealthy levels of air quality at the Klang station include carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and ozone (O3) from industrial and maritime activities, which are thought to influence PM10 concentrations in the area. When compared to MLR models, the results demonstrate that BRM are the best model for predicting the next day and next two days PM10 concentration at all locations.
      11  2