A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant

The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required st...

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Main Authors:	Ting, Sie Chun, Abdul Malik, Marlinda, Ismail, Amelia Ritahani
Format:	Article
Language:	English English English
Published:	IWA Publishing Journal 2015
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/42776/ http://irep.iium.edu.my/42776/ http://irep.iium.edu.my/42776/1/A_comparative_study_of_clonal_selection_algorithm_for.pdf http://irep.iium.edu.my/42776/4/42776_A%20comparative%20study%20of%20clonal%20selection_Scopus.pdf http://irep.iium.edu.my/42776/5/42776_A%20comparative%20study%20of%20clonal%20selection_WOS.pdf

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spelling	iium-427762017-09-27T00:41:37Z http://irep.iium.edu.my/42776/ A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant Ting, Sie Chun Abdul Malik, Marlinda Ismail, Amelia Ritahani QA75 Electronic computers. Computer science The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a wellestablished method – namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, nonlinear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP. IWA Publishing Journal 2015 Article PeerReviewed application/pdf en http://irep.iium.edu.my/42776/1/A_comparative_study_of_clonal_selection_algorithm_for.pdf application/pdf en http://irep.iium.edu.my/42776/4/42776_A%20comparative%20study%20of%20clonal%20selection_Scopus.pdf application/pdf en http://irep.iium.edu.my/42776/5/42776_A%20comparative%20study%20of%20clonal%20selection_WOS.pdf Ting, Sie Chun and Abdul Malik, Marlinda and Ismail, Amelia Ritahani (2015) A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant. Water Science and Technology, 71 (4). pp. 524-528. ISSN 0273-1223 http://www.ncbi.nlm.nih.gov/pubmed/25746643
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Ting, Sie Chun Abdul Malik, Marlinda Ismail, Amelia Ritahani A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
description	The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a wellestablished method – namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, nonlinear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP.
format	Article
author	Ting, Sie Chun Abdul Malik, Marlinda Ismail, Amelia Ritahani
author_facet	Ting, Sie Chun Abdul Malik, Marlinda Ismail, Amelia Ritahani
author_sort	Ting, Sie Chun
title	A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
title_short	A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
title_full	A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
title_fullStr	A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
title_full_unstemmed	A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
title_sort	comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant
publisher	IWA Publishing Journal
publishDate	2015
url	http://irep.iium.edu.my/42776/ http://irep.iium.edu.my/42776/ http://irep.iium.edu.my/42776/1/A_comparative_study_of_clonal_selection_algorithm_for.pdf http://irep.iium.edu.my/42776/4/42776_A%20comparative%20study%20of%20clonal%20selection_Scopus.pdf http://irep.iium.edu.my/42776/5/42776_A%20comparative%20study%20of%20clonal%20selection_WOS.pdf
first_indexed	2023-09-18T21:00:58Z
last_indexed	2023-09-18T21:00:58Z
_version_	1777410620212117504

A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant

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