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

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

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Abstract

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.

Item Type:	Article (Journal)
Additional Information:	5410/49493
Uncontrolled Keywords:	sensitivity analysis, artificial neural network, air-pollutant index
Subjects:	Q Science > QD Chemistry
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Science > Department of Marine Science
Depositing User:	Professor Dr. Kamaruzzaman Yunus
Date Deposited:	30 Mar 2016 13:07
Last Modified:	13 Apr 2017 09:40
URI:	http://irep.iium.edu.my/id/eprint/49493

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