Feed-forward artificial neural network model for air pollutant index prediction in the Southern Region of Peninsular Malaysia
| Format: | Restricted Document |
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| _version_ | 1860799797891432448 |
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| building | INTELEK Repository |
| caption | 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. |
| collection | Online Access |
| collectionurl | https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 |
| date | 2013-12-04 18:21:10 |
| format | Restricted Document |
| id | 7435 |
| institution | UniSZA |
| originalfilename | 2880-01-FH02-ESERI-16-06619.pdf |
| person | Azman Azid Hafizan Juahir Mohd Talib Latif Sharifuddin Mohd Zain Mohamad Romizan Osman |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=7435 |
| spelling | 7435 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=7435 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf 10 1.6 Adobe Acrobat Pro DC 20 Paper Capture Plug-in Azman Azid Hafizan Juahir Mohd Talib Latif Sharifuddin Mohd Zain Mohamad Romizan Osman 2013-12-04 18:21:10 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. Air Pollutant Index (API); Principal Component Analysis (PCA); Artificial Neural Network (ANN); Rotated Principal Component Scores (RPCs); Feed-Forward ANN 74 2880-01-FH02-ESERI-16-06619.pdf UniSZA Private Access Feed-forward artificial neural network model for air pollutant index prediction in the Southern Region of Peninsular Malaysia Journal of Environmental Protection 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 4 12 1-10 |
| spellingShingle | Feed-forward artificial neural network model for air pollutant index prediction in the Southern Region of Peninsular Malaysia |
| subject | 74 |
| 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 |
| title | Feed-forward artificial neural network model for air pollutant index prediction in the Southern Region of Peninsular Malaysia |
| title_full | Feed-forward artificial neural network model for air pollutant index prediction in the Southern Region of Peninsular Malaysia |
| title_fullStr | Feed-forward artificial neural network model for air pollutant index prediction in the Southern Region of Peninsular Malaysia |
| title_full_unstemmed | Feed-forward artificial neural network model for air pollutant index prediction in the Southern Region of Peninsular Malaysia |
| title_short | Feed-forward artificial neural network model for air pollutant index prediction in the Southern Region of Peninsular Malaysia |
| title_sort | feed-forward artificial neural network model for air pollutant index prediction in the southern region of peninsular malaysia |