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: | |
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| Format: | Monograph |
| Language: | English |
| Published: |
Universiti Sains Malaysia
2006
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| 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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| Summary: | 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. |
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