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

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Publication

Short-term predictions of PM₁₀ using Bayesian Regression Models

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.

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Publication

Prediction of particulate matter (PM₁₀) during high particulate event in peninsular Malaysia using Novel Hybrid Model

2023 , Izzati Amani Mohd Jafri , Norazian Mohamed Noor , Nur Alis Addiena A Rahim , Ahmad Zia Ul Saufie , György Deak Habil

High Particulate Events (HPE) contributes to the deterioration of air quality, as the fine particles present can be inhaled, leading to respiratory diseases and other health problem. Knowing the adverse effects of air pollution episodes to human health, it is crucial to create suitable models that can effectively and accurately predict air pollution concentration. This study proposed a hybrid model for forecasting the next day PM₁₀ concentration in peninsular Malaysia namely Shah Alam, Nilai, Bukit Rambai and Larkin. Hourly air pollutant concentration (PM₁₀, NOx, NO₂, SO₂, CO, O₃) and meteorological parameters (RH, T, WS) during the HPE events in 1997, 2005, 2013 and 2015 were used. Support Vector Machine (SVM) and Quantile Regression (QR) was combined to construct a hybrid models (SVM-QR) to reduce the number of input variables. Performance indicators such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Index of Agreement (d2) were used to evaluate the performance of the predictive models. SVM-QR model resulted good performance in all areas. SVM-3 was selected as the best model at Bukit Rambai (MAE=5.72, RMSE=9.71) and Shah Alam (MAE=11.89, RMSE=22.66), while SVM-1 as the best model at Larkin and Nilai with the value (MAE=7.22, RMSE=13.38) and (MAE=6.88, RMSE=11.84), respectively. This strategy was proven to help reducing the complexity of the model and enhance the predictive capacity of the model.

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Prediction of PM₁₀ level during high particulate event in Malaysia using modified model

2023 , Nur Alis Addiena A Rahim , Norazian Mohamed Noor , Izzati Amani Mohd Jafri , Ahmad Zia Ul Saufie , Boboc Madalina

Particulate matter (PM10) is one of the key indicator of air quality index (API) during high particulate event (HPE). PM10 can cause adverse effect on human health and environment; hence, it is important to develop a reliable and accurate predictive model to be used as forecasting tool to alarm the citizen especially during HPE. This study aims to develop a modified Quantile Regression (QR) model to forecast the PM10 concentration during HPE in Malaysia. The performances of three predictive models namely Multiple Linear Regression (MLR), Quantile Regression (QR) and a modified QR models i.e. combination of QR with Relief-based were compared. The hourly dataset of PM10 concentration with other gaseous pollutants and weather parameters at Klang from the year with severe haze event in Malaysia (1997, 2005, 2013 and 2015) were obtained from Department of Environment (DOE) Malaysia. Three performance measures namely Mean Absolute Error (MAE), Normalised Absolute Error (NAE) and Root Mean Squared Error (RMSE) were calculated to evaluate the accuracy of the predictive models. This study found that the Relief-QR model showed the best performance compared to MLR and QR models. The prediction of future PM10 concentration is very important because it can aid the local authorities to implement precautionary measures to limit the impact of air pollution.

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