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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書目詳細資料
主要作者: Idris, Rosli
格式: Thesis
語言:English
出版: 2007
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在線閱讀:https://ir.uitm.edu.my/id/eprint/102674/1/102674.pdf
https://ir.uitm.edu.my/id/eprint/102674/
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總結: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.