Selection of the most significant variables of air pollutants using sensitivity analysis

This study was conducted to determine the most significant parameters for the air-pollutant index (API) prediction in Malaysia using data covering a 7-year period (2006–2012) obtained from the Malaysian Department of Environment (DOE). The sensitivity analysis method coupled with the artificial neur...

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Main Authors:	Azid, Azman, Juahir, Hafizan, Toriman, Mohd Ekhwan, Endut, Azizah, Abdul Rahman, Mohd Nordin, Kamarudin, Mohd Khairul Amri, Latif, Mohd Talib, Mohd Saudi, Ahmad Shakir, Che Hasnam, Che Noraini, Yunus, Kamaruzzaman
Format:	Article
Language:	English English
Published:	ASTM International 2016
Subjects:	QD Chemistry
Online Access:	http://irep.iium.edu.my/49493/ http://irep.iium.edu.my/49493/ http://irep.iium.edu.my/49493/ http://irep.iium.edu.my/49493/1/Selection_of_the_Most_Significant_Variables_of_Air_Pollutants_Using_Sensitivity_Analysis.pdf http://irep.iium.edu.my/49493/4/59493_Selection%20of%20the%20most%20significant%20variables_Scopus.pdf

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recordtype	eprints
spelling	iium-494932017-04-13T01:40:52Z http://irep.iium.edu.my/49493/ Selection of the most significant variables of air pollutants using sensitivity analysis Azid, Azman Juahir, Hafizan Toriman, Mohd Ekhwan Endut, Azizah Abdul Rahman, Mohd Nordin Kamarudin, Mohd Khairul Amri Latif, Mohd Talib Mohd Saudi, Ahmad Shakir Che Hasnam, Che Noraini Yunus, Kamaruzzaman QD Chemistry This study was conducted to determine the most significant parameters for the air-pollutant index (API) prediction in Malaysia using data covering a 7-year period (2006–2012) obtained from the Malaysian Department of Environment (DOE). The sensitivity analysis method coupled with the artificial neural network (ANN) was applied. Nine models (ANN-API-AP, ANN-API-LCO, ANN-API-LO3, ANN-API-LPM10, ANN-API-LSO2, ANN-API-LNO2, ANN-API-LCH4, ANN-APILNmHC and ANN-API-LTHC) were carried out in the sensitivity analysis test. From the findings, PM10 and CO were identified as the most significant parameters in Malaysia. Three artificial neural network models (ANN-API-AP, ANN-API-LO, and ANN-API-DOE) were compared based on the performance criterion [R2, root-mean-square error (RMSE), and squared sum of all errors (SSE)] for the best prediction model selection. The ANN-API-AP, ANN-API-LO, and ANN-APIDOE models have R2 values of 0.733, 0.578, and 0.742, respectively; RMSE values of 8.689, 10.858, and 8.357, respectively; SSE values of 762,767.22, 191,280.60, and 705,600.05, respectively. The findings exhibit the ANN-API-LO model has a lower value in R2 and higher values in RMSE and SSE than others. ANN-API-LO model was considered as the best model of prediction because of fewer variables was utilized as input and far less complex than others. Hence, the use of fewer parameters of the API prediction has been highly practicable for air resource management because of its time and cost efficiency. ASTM International 2016 Article PeerReviewed application/pdf en http://irep.iium.edu.my/49493/1/Selection_of_the_Most_Significant_Variables_of_Air_Pollutants_Using_Sensitivity_Analysis.pdf application/pdf en http://irep.iium.edu.my/49493/4/59493_Selection%20of%20the%20most%20significant%20variables_Scopus.pdf Azid, Azman and Juahir, Hafizan and Toriman, Mohd Ekhwan and Endut, Azizah and Abdul Rahman, Mohd Nordin and Kamarudin, Mohd Khairul Amri and Latif, Mohd Talib and Mohd Saudi, Ahmad Shakir and Che Hasnam, Che Noraini and Yunus, Kamaruzzaman (2016) Selection of the most significant variables of air pollutants using sensitivity analysis. Journal of Testing and Evaluation, 44 (1). pp. 376-384. ISSN 0090-3973 http://www.astm.org/DIGITAL_LIBRARY/JOURNALS/TESTEVAL/PAGES/JTE20140325.htm 10.1520/JTE20140325
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	QD Chemistry
spellingShingle	QD Chemistry Azid, Azman Juahir, Hafizan Toriman, Mohd Ekhwan Endut, Azizah Abdul Rahman, Mohd Nordin Kamarudin, Mohd Khairul Amri Latif, Mohd Talib Mohd Saudi, Ahmad Shakir Che Hasnam, Che Noraini Yunus, Kamaruzzaman Selection of the most significant variables of air pollutants using sensitivity analysis
description	This study was conducted to determine the most significant parameters for the air-pollutant index (API) prediction in Malaysia using data covering a 7-year period (2006–2012) obtained from the Malaysian Department of Environment (DOE). The sensitivity analysis method coupled with the artificial neural network (ANN) was applied. Nine models (ANN-API-AP, ANN-API-LCO, ANN-API-LO3, ANN-API-LPM10, ANN-API-LSO2, ANN-API-LNO2, ANN-API-LCH4, ANN-APILNmHC and ANN-API-LTHC) were carried out in the sensitivity analysis test. From the findings, PM10 and CO were identified as the most significant parameters in Malaysia. Three artificial neural network models (ANN-API-AP, ANN-API-LO, and ANN-API-DOE) were compared based on the performance criterion [R2, root-mean-square error (RMSE), and squared sum of all errors (SSE)] for the best prediction model selection. The ANN-API-AP, ANN-API-LO, and ANN-APIDOE models have R2 values of 0.733, 0.578, and 0.742, respectively; RMSE values of 8.689, 10.858, and 8.357, respectively; SSE values of 762,767.22, 191,280.60, and 705,600.05, respectively. The findings exhibit the ANN-API-LO model has a lower value in R2 and higher values in RMSE and SSE than others. ANN-API-LO model was considered as the best model of prediction because of fewer variables was utilized as input and far less complex than others. Hence, the use of fewer parameters of the API prediction has been highly practicable for air resource management because of its time and cost efficiency.
format	Article
author	Azid, Azman Juahir, Hafizan Toriman, Mohd Ekhwan Endut, Azizah Abdul Rahman, Mohd Nordin Kamarudin, Mohd Khairul Amri Latif, Mohd Talib Mohd Saudi, Ahmad Shakir Che Hasnam, Che Noraini Yunus, Kamaruzzaman
author_facet	Azid, Azman Juahir, Hafizan Toriman, Mohd Ekhwan Endut, Azizah Abdul Rahman, Mohd Nordin Kamarudin, Mohd Khairul Amri Latif, Mohd Talib Mohd Saudi, Ahmad Shakir Che Hasnam, Che Noraini Yunus, Kamaruzzaman
author_sort	Azid, Azman
title	Selection of the most significant variables of air pollutants using sensitivity analysis
title_short	Selection of the most significant variables of air pollutants using sensitivity analysis
title_full	Selection of the most significant variables of air pollutants using sensitivity analysis
title_fullStr	Selection of the most significant variables of air pollutants using sensitivity analysis
title_full_unstemmed	Selection of the most significant variables of air pollutants using sensitivity analysis
title_sort	selection of the most significant variables of air pollutants using sensitivity analysis
publisher	ASTM International
publishDate	2016
url	http://irep.iium.edu.my/49493/ http://irep.iium.edu.my/49493/ http://irep.iium.edu.my/49493/ http://irep.iium.edu.my/49493/1/Selection_of_the_Most_Significant_Variables_of_Air_Pollutants_Using_Sensitivity_Analysis.pdf http://irep.iium.edu.my/49493/4/59493_Selection%20of%20the%20most%20significant%20variables_Scopus.pdf
first_indexed	2023-09-18T21:09:58Z
last_indexed	2023-09-18T21:09:58Z
_version_	1777411186827984896

Selection of the most significant variables of air pollutants using sensitivity analysis

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