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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: Azman, Azid, Mohd Khairul Amri, Kamarudin, Hafizan, Juahir
Format: Article
Language:English
English
English
Published: 2016
Subjects:
HF Commerce
Online Access:http://eprints.unisza.edu.my/7186/1/FH02-ESERI-16-05416.pdf
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spelling my-unisza-ir.71862022-09-13T05:33:39Z http://eprints.unisza.edu.my/7186/ Selection of the Most Significant Variables of Air Pollutants Using Sensitivity Analysis Azman, Azid Mohd Khairul Amri, Kamarudin Hafizan, Juahir HF Commerce 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, 1,191,280.60, 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. 2016 Article PeerReviewed text en http://eprints.unisza.edu.my/7186/1/FH02-ESERI-16-05416.pdf image en http://eprints.unisza.edu.my/7186/2/FH02-ESERI-16-05467.jpg image en http://eprints.unisza.edu.my/7186/3/FH02-ESERI-16-06092.jpg Azman, Azid and Mohd Khairul Amri, Kamarudin and Hafizan, Juahir (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
institution Universiti Sultan Zainal Abidin
building UNISZA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sultan Zainal Abidin
content_source UNISZA Institutional Repository
url_provider https://eprints.unisza.edu.my/
language English
English
English
topic HF Commerce
spellingShingle HF Commerce
Azman, Azid
Mohd Khairul Amri, Kamarudin
Hafizan, Juahir
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, 1,191,280.60, 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 Azman, Azid
Mohd Khairul Amri, Kamarudin
Hafizan, Juahir
author_facet Azman, Azid
Mohd Khairul Amri, Kamarudin
Hafizan, Juahir
author_sort Azman, Azid
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
publishDate 2016
url http://eprints.unisza.edu.my/7186/1/FH02-ESERI-16-05416.pdf
http://eprints.unisza.edu.my/7186/2/FH02-ESERI-16-05467.jpg
http://eprints.unisza.edu.my/7186/3/FH02-ESERI-16-06092.jpg
http://eprints.unisza.edu.my/7186/
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score 13.211869

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