Feed-forward artificial neural network model for air pollutant index prediction in the Southern Region of Peninsular 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)...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.unisza.edu.my/2880/1/FH02-ESERI-16-06619.pdf http://eprints.unisza.edu.my/2880/ |
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| Summary: | 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, O3, PM10, NO2, CH4, 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 |
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