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 Saudi, A. S. M. and Hasnam, C N C (2016) Selection of the Most Significant Variables of Air Pollutants Using Sensitivity Analysis. Journal of Testing and Evaluation, 44 (1). pp. 1-10. ISSN 0090-3973 (In Press)

[img] Text
Selection of the Most Significant Variables of Air Pollutants Using Sensitivity Analysis.pdf
Restricted to Registered users only

Download (996Kb) | Request a copy
Official URL: http://www.astm.org/

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, 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.

Item Type: Article
Keywords: sensitivity analysis, artificial neural network, air-pollutant index
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
H Social Sciences > HA Statistics
Q Science > Q Science (General)
Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Computer software
Faculty / Institute: East Coast Environmental Research Institute (ESERI)
Depositing User: Dr Azman Azid
Date Deposited: 15 Nov 2015 04:24
Last Modified: 20 Sep 2016 02:25
URI: http://erep.unisza.edu.my/id/eprint/4000

Actions (login required)

View Item View Item