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)
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Abstract
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.
| Item Type: | Monograph (Project Report) |
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| Subjects: | T Technology T Technology > TK Electrical Engineering. Electronics. Nuclear Engineering |
| Divisions: | Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraaan Elektrik & Elektronik (School of Electrical & Electronic Engineering) > Monograph |
| Depositing User: | Mr Engku Shahidil Engku Ab Rahman |
| Date Deposited: | 22 May 2023 09:47 |
| Last Modified: | 22 May 2023 09:47 |
| URI: | http://eprints.usm.my/id/eprint/58675 |
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