Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support

Air quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM<sub<10</sub<) throughout the years. Studies have affirmed that PM<sub<10</sub< influence human health and the environment. Therefore, precise forecasting algorithms are urgently...
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Autor*in:	Samsuri Abdullah [verfasserIn] Marzuki Ismail [verfasserIn] Ali Najah Ahmed [verfasserIn] Ahmad Makmom Abdullah [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	particulate matter forecasting air quality malaysia

Übergeordnetes Werk:	In: Atmosphere - MDPI AG, 2011, 10(2019), 11, p 667
Übergeordnetes Werk:	volume:10 ; year:2019 ; number:11, p 667

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/atmos10110667

Katalog-ID:	DOAJ033650268

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520			\|a Air quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM<sub<10</sub<) throughout the years. Studies have affirmed that PM<sub<10</sub< influence human health and the environment. Therefore, precise forecasting algorithms are urgently needed to determine the PM<sub<10</sub< status for mitigation plan and early warning purposes. This study investigates the forecasting performance of a linear (Multiple Linear Regression) and two non-linear models (Multi-Layer Perceptron and Radial Basis Function) utilizing meteorological and gaseous pollutants variables as input parameters from the year 2000−2014 at four sites with different surrounding activities of urban, sub-urban and rural areas. Non-linear model (Radial Basis Function) outperforms the linear model with the error reduced by 78.9% (urban), 32.1% (sub-urban) and 39.8% (rural). Association between PM<sub<10</sub< and its contributing factors are complex and non-linear in nature, best captured by an Artificial Neural Network, which generates more accurate PM<sub<10</sub< compared to the linear model. The results are robust enough for precise next day forecasting of PM<sub<10</sub< concentration on the East Coast of Peninsular Malaysia.
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spelling	10.3390/atmos10110667 doi (DE-627)DOAJ033650268 (DE-599)DOAJ48ae32f3ae8b4382b0d2c9e09798757c DE-627 ger DE-627 rakwb eng QC851-999 Samsuri Abdullah verfasserin aut Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Air quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM<sub<10</sub<) throughout the years. Studies have affirmed that PM<sub<10</sub< influence human health and the environment. Therefore, precise forecasting algorithms are urgently needed to determine the PM<sub<10</sub< status for mitigation plan and early warning purposes. This study investigates the forecasting performance of a linear (Multiple Linear Regression) and two non-linear models (Multi-Layer Perceptron and Radial Basis Function) utilizing meteorological and gaseous pollutants variables as input parameters from the year 2000−2014 at four sites with different surrounding activities of urban, sub-urban and rural areas. Non-linear model (Radial Basis Function) outperforms the linear model with the error reduced by 78.9% (urban), 32.1% (sub-urban) and 39.8% (rural). Association between PM<sub<10</sub< and its contributing factors are complex and non-linear in nature, best captured by an Artificial Neural Network, which generates more accurate PM<sub<10</sub< compared to the linear model. The results are robust enough for precise next day forecasting of PM<sub<10</sub< concentration on the East Coast of Peninsular Malaysia. particulate matter forecasting air quality malaysia Meteorology. Climatology Marzuki Ismail verfasserin aut Ali Najah Ahmed verfasserin aut Ahmad Makmom Abdullah verfasserin aut In Atmosphere MDPI AG, 2011 10(2019), 11, p 667 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:10 year:2019 number:11, p 667 https://doi.org/10.3390/atmos10110667 kostenfrei https://doaj.org/article/48ae32f3ae8b4382b0d2c9e09798757c kostenfrei https://www.mdpi.com/2073-4433/10/11/667 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2019 11, p 667
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allfieldsGer	10.3390/atmos10110667 doi (DE-627)DOAJ033650268 (DE-599)DOAJ48ae32f3ae8b4382b0d2c9e09798757c DE-627 ger DE-627 rakwb eng QC851-999 Samsuri Abdullah verfasserin aut Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Air quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM<sub<10</sub<) throughout the years. Studies have affirmed that PM<sub<10</sub< influence human health and the environment. Therefore, precise forecasting algorithms are urgently needed to determine the PM<sub<10</sub< status for mitigation plan and early warning purposes. This study investigates the forecasting performance of a linear (Multiple Linear Regression) and two non-linear models (Multi-Layer Perceptron and Radial Basis Function) utilizing meteorological and gaseous pollutants variables as input parameters from the year 2000−2014 at four sites with different surrounding activities of urban, sub-urban and rural areas. Non-linear model (Radial Basis Function) outperforms the linear model with the error reduced by 78.9% (urban), 32.1% (sub-urban) and 39.8% (rural). Association between PM<sub<10</sub< and its contributing factors are complex and non-linear in nature, best captured by an Artificial Neural Network, which generates more accurate PM<sub<10</sub< compared to the linear model. The results are robust enough for precise next day forecasting of PM<sub<10</sub< concentration on the East Coast of Peninsular Malaysia. particulate matter forecasting air quality malaysia Meteorology. Climatology Marzuki Ismail verfasserin aut Ali Najah Ahmed verfasserin aut Ahmad Makmom Abdullah verfasserin aut In Atmosphere MDPI AG, 2011 10(2019), 11, p 667 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:10 year:2019 number:11, p 667 https://doi.org/10.3390/atmos10110667 kostenfrei https://doaj.org/article/48ae32f3ae8b4382b0d2c9e09798757c kostenfrei https://www.mdpi.com/2073-4433/10/11/667 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2019 11, p 667
allfieldsSound	10.3390/atmos10110667 doi (DE-627)DOAJ033650268 (DE-599)DOAJ48ae32f3ae8b4382b0d2c9e09798757c DE-627 ger DE-627 rakwb eng QC851-999 Samsuri Abdullah verfasserin aut Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Air quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM<sub<10</sub<) throughout the years. Studies have affirmed that PM<sub<10</sub< influence human health and the environment. Therefore, precise forecasting algorithms are urgently needed to determine the PM<sub<10</sub< status for mitigation plan and early warning purposes. This study investigates the forecasting performance of a linear (Multiple Linear Regression) and two non-linear models (Multi-Layer Perceptron and Radial Basis Function) utilizing meteorological and gaseous pollutants variables as input parameters from the year 2000−2014 at four sites with different surrounding activities of urban, sub-urban and rural areas. Non-linear model (Radial Basis Function) outperforms the linear model with the error reduced by 78.9% (urban), 32.1% (sub-urban) and 39.8% (rural). Association between PM<sub<10</sub< and its contributing factors are complex and non-linear in nature, best captured by an Artificial Neural Network, which generates more accurate PM<sub<10</sub< compared to the linear model. The results are robust enough for precise next day forecasting of PM<sub<10</sub< concentration on the East Coast of Peninsular Malaysia. particulate matter forecasting air quality malaysia Meteorology. Climatology Marzuki Ismail verfasserin aut Ali Najah Ahmed verfasserin aut Ahmad Makmom Abdullah verfasserin aut In Atmosphere MDPI AG, 2011 10(2019), 11, p 667 (DE-627)657584010 (DE-600)2605928-9 20734433 nnns volume:10 year:2019 number:11, p 667 https://doi.org/10.3390/atmos10110667 kostenfrei https://doaj.org/article/48ae32f3ae8b4382b0d2c9e09798757c kostenfrei https://www.mdpi.com/2073-4433/10/11/667 kostenfrei https://doaj.org/toc/2073-4433 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2019 11, p 667
language	English
source	In Atmosphere 10(2019), 11, p 667 volume:10 year:2019 number:11, p 667
sourceStr	In Atmosphere 10(2019), 11, p 667 volume:10 year:2019 number:11, p 667
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	particulate matter forecasting air quality malaysia Meteorology. Climatology
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container_title	Atmosphere
authorswithroles_txt_mv	Samsuri Abdullah @@aut@@ Marzuki Ismail @@aut@@ Ali Najah Ahmed @@aut@@ Ahmad Makmom Abdullah @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
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callnumber-first	Q - Science
author	Samsuri Abdullah
spellingShingle	Samsuri Abdullah misc QC851-999 misc particulate matter misc forecasting misc air quality misc malaysia misc Meteorology. Climatology Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support
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topic_title	QC851-999 Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support particulate matter forecasting air quality malaysia
topic	misc QC851-999 misc particulate matter misc forecasting misc air quality misc malaysia misc Meteorology. Climatology
topic_unstemmed	misc QC851-999 misc particulate matter misc forecasting misc air quality misc malaysia misc Meteorology. Climatology
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title	Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support
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title_full	Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support
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author_browse	Samsuri Abdullah Marzuki Ismail Ali Najah Ahmed Ahmad Makmom Abdullah
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title_sort	forecasting particulate matter concentration using linear and non-linear approaches for air quality decision support
callnumber	QC851-999
title_auth	Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support
abstract	Air quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM<sub<10</sub<) throughout the years. Studies have affirmed that PM<sub<10</sub< influence human health and the environment. Therefore, precise forecasting algorithms are urgently needed to determine the PM<sub<10</sub< status for mitigation plan and early warning purposes. This study investigates the forecasting performance of a linear (Multiple Linear Regression) and two non-linear models (Multi-Layer Perceptron and Radial Basis Function) utilizing meteorological and gaseous pollutants variables as input parameters from the year 2000−2014 at four sites with different surrounding activities of urban, sub-urban and rural areas. Non-linear model (Radial Basis Function) outperforms the linear model with the error reduced by 78.9% (urban), 32.1% (sub-urban) and 39.8% (rural). Association between PM<sub<10</sub< and its contributing factors are complex and non-linear in nature, best captured by an Artificial Neural Network, which generates more accurate PM<sub<10</sub< compared to the linear model. The results are robust enough for precise next day forecasting of PM<sub<10</sub< concentration on the East Coast of Peninsular Malaysia.
abstractGer	Air quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM<sub<10</sub<) throughout the years. Studies have affirmed that PM<sub<10</sub< influence human health and the environment. Therefore, precise forecasting algorithms are urgently needed to determine the PM<sub<10</sub< status for mitigation plan and early warning purposes. This study investigates the forecasting performance of a linear (Multiple Linear Regression) and two non-linear models (Multi-Layer Perceptron and Radial Basis Function) utilizing meteorological and gaseous pollutants variables as input parameters from the year 2000−2014 at four sites with different surrounding activities of urban, sub-urban and rural areas. Non-linear model (Radial Basis Function) outperforms the linear model with the error reduced by 78.9% (urban), 32.1% (sub-urban) and 39.8% (rural). Association between PM<sub<10</sub< and its contributing factors are complex and non-linear in nature, best captured by an Artificial Neural Network, which generates more accurate PM<sub<10</sub< compared to the linear model. The results are robust enough for precise next day forecasting of PM<sub<10</sub< concentration on the East Coast of Peninsular Malaysia.
abstract_unstemmed	Air quality status on the East Coast of Peninsular Malaysia is dominated by Particulate Matter (PM<sub<10</sub<) throughout the years. Studies have affirmed that PM<sub<10</sub< influence human health and the environment. Therefore, precise forecasting algorithms are urgently needed to determine the PM<sub<10</sub< status for mitigation plan and early warning purposes. This study investigates the forecasting performance of a linear (Multiple Linear Regression) and two non-linear models (Multi-Layer Perceptron and Radial Basis Function) utilizing meteorological and gaseous pollutants variables as input parameters from the year 2000−2014 at four sites with different surrounding activities of urban, sub-urban and rural areas. Non-linear model (Radial Basis Function) outperforms the linear model with the error reduced by 78.9% (urban), 32.1% (sub-urban) and 39.8% (rural). Association between PM<sub<10</sub< and its contributing factors are complex and non-linear in nature, best captured by an Artificial Neural Network, which generates more accurate PM<sub<10</sub< compared to the linear model. The results are robust enough for precise next day forecasting of PM<sub<10</sub< concentration on the East Coast of Peninsular Malaysia.
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container_issue	11, p 667
title_short	Forecasting Particulate Matter Concentration Using Linear and Non-Linear Approaches for Air Quality Decision Support
url	https://doi.org/10.3390/atmos10110667 https://doaj.org/article/48ae32f3ae8b4382b0d2c9e09798757c https://www.mdpi.com/2073-4433/10/11/667 https://doaj.org/toc/2073-4433
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up_date	2024-07-03T18:56:13.589Z
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