Analysis of air quality index (AQI) in Klang valley using artificial neural network (ANN) technique / Rosli Idris
This project investigates and analyzes the effectiveness of Artificial Neural Networks (ANN) technique in predicting the Air Quality Index (AQI) in Klang Valley. The ANN technique simplifies and speeds up the computation of the AQI, as compared to the current existing method used by Department of En...
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| 言語: | English |
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2007
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| オンライン･アクセス: | https://ir.uitm.edu.my/id/eprint/102674/1/102674.pdf https://ir.uitm.edu.my/id/eprint/102674/ |
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my.uitm.ir.1026742025-01-15T06:19:30Z https://ir.uitm.edu.my/id/eprint/102674/ Analysis of air quality index (AQI) in Klang valley using artificial neural network (ANN) technique / Rosli Idris Idris, Rosli Air pollution and its control This project investigates and analyzes the effectiveness of Artificial Neural Networks (ANN) technique in predicting the Air Quality Index (AQI) in Klang Valley. The ANN technique simplifies and speeds up the computation of the AQI, as compared to the current existing method used by Department of Environment (DOE). In the ANN technique, three methods will be used. The methods are Levenberg-Marquardt Algorithms, Resilient Backpropagation and Quasi-Newton Algorithms will be considered adopted to analyze the AQI data. Between these three methods, the Levenberg-Marquardt Algorithms is the best method for analyzing AQI data with the lowest error of data during training process which is from -0.5569 to 0.5787 and also has the fastest learning or training the AQI data. 2007 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/102674/1/102674.pdf Analysis of air quality index (AQI) in Klang valley using artificial neural network (ANN) technique / Rosli Idris. (2007) Degree thesis, thesis, Universiti Teknologi MARA (UiTM). <http://terminalib.uitm.edu.my/102674.pdf> |
| institution |
Universiti Teknologi Mara |
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Tun Abdul Razak Library |
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Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Teknologi Mara |
| content_source |
UiTM Institutional Repository |
| url_provider |
http://ir.uitm.edu.my/ |
| language |
English |
| topic |
Air pollution and its control |
| spellingShingle |
Air pollution and its control Idris, Rosli Analysis of air quality index (AQI) in Klang valley using artificial neural network (ANN) technique / Rosli Idris |
| description |
This project investigates and analyzes the effectiveness of Artificial Neural Networks (ANN) technique in predicting the Air Quality Index (AQI) in Klang Valley. The ANN technique simplifies and speeds up the computation of the AQI, as compared to the current existing method used by Department of Environment (DOE). In the ANN technique, three methods will be used. The methods are Levenberg-Marquardt Algorithms, Resilient Backpropagation and Quasi-Newton Algorithms will be considered adopted to analyze the AQI data. Between these three methods, the Levenberg-Marquardt Algorithms is the best method for analyzing AQI data with the lowest error of data during training process which is from -0.5569 to 0.5787 and also has the fastest learning or training the AQI data. |
| format |
Thesis |
| author |
Idris, Rosli |
| author_facet |
Idris, Rosli |
| author_sort |
Idris, Rosli |
| title |
Analysis of air quality index (AQI) in Klang valley using artificial neural network (ANN) technique / Rosli Idris |
| title_short |
Analysis of air quality index (AQI) in Klang valley using artificial neural network (ANN) technique / Rosli Idris |
| title_full |
Analysis of air quality index (AQI) in Klang valley using artificial neural network (ANN) technique / Rosli Idris |
| title_fullStr |
Analysis of air quality index (AQI) in Klang valley using artificial neural network (ANN) technique / Rosli Idris |
| title_full_unstemmed |
Analysis of air quality index (AQI) in Klang valley using artificial neural network (ANN) technique / Rosli Idris |
| title_sort |
analysis of air quality index (aqi) in klang valley using artificial neural network (ann) technique / rosli idris |
| publishDate |
2007 |
| url |
https://ir.uitm.edu.my/id/eprint/102674/1/102674.pdf https://ir.uitm.edu.my/id/eprint/102674/ |
| _version_ |
1823097809135468544 |
| score |
13.251813 |