Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia — Oak Academic Publishing
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Feed-Forward Artificial Neural Network Model for Air Pollutant Index Prediction in the Southern Region of Peninsular Malaysia
East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, Kuala Terengganu, Malaysia
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East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, Kuala Terengganu, Malaysia
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School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
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Department of Chemistry, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
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Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Malaysia
1 East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, Kuala Terengganu, Malaysia
2 East Coast Environmental Research Institute (ESERI), Universiti Sultan Zainal Abidin, Gong Badak Campus, Kuala Terengganu, Malaysia
3 School of Environmental and Natural Resource Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
4 Department of Chemistry, Faculty of Science, Universiti Malaya, Kuala Lumpur, Malaysia
5 Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Malaysia
This paper describes the application of principal component analysis (PCA) and artificial neural network (ANN) to predict the air pollutant index (API) within the seven selected Malaysian air monitoring stations in the southern region of Peninsular Malaysia based on seven years database (2005-2011). Feed-forward ANN was used as a prediction method. The feed-forward ANN analysis demonstrated that the rotated principal component scores (RPCs) were the best input parameters to predict API. From the 4 RPCs, only 10 (CO, O 3 , PM 10 , NO 2 , CH 4 , NmHC, THC, wind direction, humidity and ambient temp) out of 12 prediction variables were the most significant parameters to predict API. The results proved that the ANN method can be applied successfully as tools for decision making and problem solving for better atmospheric management.
KeywordsAir Pollutant Index (API)Principal Component Analysis (PCA)Artificial Neural Network (ANN)Rotated Principal Component Scores (RPCs)Feed-Forward ANN
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