Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia

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...

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Published in:Atmosphere
Main Author: Ramli N.; Abdul Hamid H.; Yahaya A.S.; Ul-Saufie A.Z.; Mohamed Noor N.; Abu Seman N.A.; Kamarudzaman A.N.; Deák G.
Format: Article
Language:English
Published: MDPI 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149006880&doi=10.3390%2fatmos14020311&partnerID=40&md5=c8b4e29d0ab331ba39c58748ee16bf7d
id 2-s2.0-85149006880
spelling 2-s2.0-85149006880
Ramli N.; Abdul Hamid H.; Yahaya A.S.; Ul-Saufie A.Z.; Mohamed Noor N.; Abu Seman N.A.; Kamarudzaman A.N.; Deák G.
Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia
2023
Atmosphere
14
2
10.3390/atmos14020311
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149006880&doi=10.3390%2fatmos14020311&partnerID=40&md5=c8b4e29d0ab331ba39c58748ee16bf7d
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. © 2023 by the authors.
MDPI
20734433
English
Article
All Open Access; Gold Open Access
author Ramli N.; Abdul Hamid H.; Yahaya A.S.; Ul-Saufie A.Z.; Mohamed Noor N.; Abu Seman N.A.; Kamarudzaman A.N.; Deák G.
spellingShingle Ramli N.; Abdul Hamid H.; Yahaya A.S.; Ul-Saufie A.Z.; Mohamed Noor N.; Abu Seman N.A.; Kamarudzaman A.N.; Deák G.
Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia
author_facet Ramli N.; Abdul Hamid H.; Yahaya A.S.; Ul-Saufie A.Z.; Mohamed Noor N.; Abu Seman N.A.; Kamarudzaman A.N.; Deák G.
author_sort Ramli N.; Abdul Hamid H.; Yahaya A.S.; Ul-Saufie A.Z.; Mohamed Noor N.; Abu Seman N.A.; Kamarudzaman A.N.; Deák G.
title Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia
title_short Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia
title_full Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia
title_fullStr Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia
title_full_unstemmed Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia
title_sort Performance of Bayesian Model Averaging (BMA) for Short-Term Prediction of PM10 Concentration in the Peninsular Malaysia
publishDate 2023
container_title Atmosphere
container_volume 14
container_issue 2
doi_str_mv 10.3390/atmos14020311
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85149006880&doi=10.3390%2fatmos14020311&partnerID=40&md5=c8b4e29d0ab331ba39c58748ee16bf7d
description 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. © 2023 by the authors.
publisher MDPI
issn 20734433
language English
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accesstype All Open Access; Gold Open Access
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