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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Bibliographic Details
Main Author:	Idris, Rosli
Format:	Thesis
Language:	English
Published:	2007
Subjects:	Air pollution and its control
Online Access:	https://ir.uitm.edu.my/id/eprint/102674/1/102674.pdf
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id	my-uitm-ir.102674
record_format	uketd_dc
spelling	my-uitm-ir.1026742024-11-26T06:56:14Z Analysis of air quality index (AQI) in Klang valley using artificial neural network (ANN) technique / Rosli Idris 2007 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 https://ir.uitm.edu.my/id/eprint/102674/ https://ir.uitm.edu.my/id/eprint/102674/1/102674.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
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
qualification_level	Bachelor degree
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
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2007
url	https://ir.uitm.edu.my/id/eprint/102674/1/102674.pdf
_version_	1818588044198936576

Analysis of air quality index (AQI) in Klang valley using artificial neural network (ANN) technique / Rosli Idris

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