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Development Of An Intelligent System For River Water Quality Classification Based On Algae Composition

Throughout the years, many researches have been conducted on the potential applications of Artificial Intelligence (AI) in the biological monitoring of river quality. This project will provide an overview regarding the feasibility of the application of neural networks for direct classification...

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Main Author: Fong, Wai Mei
Format: Monograph
Language:English
Published: Universiti Sains Malaysia 2006
Subjects:
T Technology
TK Electrical Engineering. Electronics. Nuclear Engineering
Online Access:http://eprints.usm.my/58675/1/Development%20Of%20An%20Intelligent%20System%20For%20River%20Water%20Quality%20Classification%20Based%20On%20Algae%20Composition_Fong%20Wai%20Mei.pdf
http://eprints.usm.my/58675/
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spelling my.usm.eprints.58675 http://eprints.usm.my/58675/ Development Of An Intelligent System For River Water Quality Classification Based On Algae Composition Fong, Wai Mei T Technology TK Electrical Engineering. Electronics. Nuclear Engineering Throughout the years, many researches have been conducted on the potential applications of Artificial Intelligence (AI) in the biological monitoring of river quality. This project will provide an overview regarding the feasibility of the application of neural networks for direct classification of river water quality based on algae composition. A brief introduction to neural networks and the suitability of neural network for use in river water quality determination will be investigated. In this project, several neural networks will be developed and their performance are compared to yield the most suitable network that will be used to model the classification system for determination of river water quality based on algae composition. Among the types of neural network that will be developed are Multilayer Perceptron network (MLP), Radial Basis Function (RBF) network and Hybrid Multilayer Perceptron (HMLP) network. This study proves that the HMLP network trained using the MRPE algorithm achieves the best performance as compared to the MLP and RBF network. The HMLP network produces 90% accuracy. In this study, an intelligent system is developed for the classification of river water quality using the HMLP network. The proposed system provides several advantages in terms of its applicability, high accuracy, user-friendliness and as well as yields faster results compared to conventional system. Universiti Sains Malaysia 2006-03-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/58675/1/Development%20Of%20An%20Intelligent%20System%20For%20River%20Water%20Quality%20Classification%20Based%20On%20Algae%20Composition_Fong%20Wai%20Mei.pdf Fong, Wai Mei (2006) Development Of An Intelligent System For River Water Quality Classification Based On Algae Composition. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Elektrik dan Elektronik. (Submitted)
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TK Electrical Engineering. Electronics. Nuclear Engineering
spellingShingle T Technology
TK Electrical Engineering. Electronics. Nuclear Engineering
Fong, Wai Mei
Development Of An Intelligent System For River Water Quality Classification Based On Algae Composition
description Throughout the years, many researches have been conducted on the potential applications of Artificial Intelligence (AI) in the biological monitoring of river quality. This project will provide an overview regarding the feasibility of the application of neural networks for direct classification of river water quality based on algae composition. A brief introduction to neural networks and the suitability of neural network for use in river water quality determination will be investigated. In this project, several neural networks will be developed and their performance are compared to yield the most suitable network that will be used to model the classification system for determination of river water quality based on algae composition. Among the types of neural network that will be developed are Multilayer Perceptron network (MLP), Radial Basis Function (RBF) network and Hybrid Multilayer Perceptron (HMLP) network. This study proves that the HMLP network trained using the MRPE algorithm achieves the best performance as compared to the MLP and RBF network. The HMLP network produces 90% accuracy. In this study, an intelligent system is developed for the classification of river water quality using the HMLP network. The proposed system provides several advantages in terms of its applicability, high accuracy, user-friendliness and as well as yields faster results compared to conventional system.
format Monograph
author Fong, Wai Mei
author_facet Fong, Wai Mei
author_sort Fong, Wai Mei
title Development Of An Intelligent System For River Water Quality Classification Based On Algae Composition
title_short Development Of An Intelligent System For River Water Quality Classification Based On Algae Composition
title_full Development Of An Intelligent System For River Water Quality Classification Based On Algae Composition
title_fullStr Development Of An Intelligent System For River Water Quality Classification Based On Algae Composition
title_full_unstemmed Development Of An Intelligent System For River Water Quality Classification Based On Algae Composition
title_sort development of an intelligent system for river water quality classification based on algae composition
publisher Universiti Sains Malaysia
publishDate 2006
url http://eprints.usm.my/58675/1/Development%20Of%20An%20Intelligent%20System%20For%20River%20Water%20Quality%20Classification%20Based%20On%20Algae%20Composition_Fong%20Wai%20Mei.pdf
http://eprints.usm.my/58675/
_version_ 1768007916074303488
score 13.211869

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