Supervised and unsupervised artificial neural networks for analysis of diatom abundance in tropical Putrajaya Lake, Malaysia

Five years of data from 2001 until 2006 of warm unstratified shallow, oligotrophic to mesothropic tropical Putrajaya Lake, Malaysia were used to study pattern discovery and forecasting of the diatom abundance using supervised and unsupervised artificial neural networks. Recurrent artificial neural n...

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Main Authors:	Sorayya, M, Aishah, Mohd. Sapiyan, B
Format:	Article
Language:	English
Published:	Universiti Kebangsaan Malaysia 2012
Online Access:	http://journalarticle.ukm.my/5414/ http://journalarticle.ukm.my/5414/ http://journalarticle.ukm.my/5414/1/01%2520M.sorayya.pdf

id	ukm-5414
recordtype	eprints
spelling	ukm-54142016-12-14T06:38:23Z http://journalarticle.ukm.my/5414/ Supervised and unsupervised artificial neural networks for analysis of diatom abundance in tropical Putrajaya Lake, Malaysia Sorayya, M Aishah, Mohd. Sapiyan, B Five years of data from 2001 until 2006 of warm unstratified shallow, oligotrophic to mesothropic tropical Putrajaya Lake, Malaysia were used to study pattern discovery and forecasting of the diatom abundance using supervised and unsupervised artificial neural networks. Recurrent artificial neural network (RANN) was used for the supervised artificial neural network and Kohonen Self Organizing Feature Maps (SOM) was used for unsupervised artificial neural network. RANN was applied for forecasting of diatom abundance. The RANN performance was measured in terms of root mean square error (RMSE) and the value reported was 29.12 cell/mL. Classification and clustering by SOM and sensitivity analysis from the RANN were used to reveal the relationship among water temperature, pH, nitrate nitrogen (NO3-N) concentration, chemical oxygen demand (COD) concentration and diatom abundance. The results indicated that the combination of supervised and unsupervised artificial neural network is important not only for forecasting algae abundance but also in reasoning and understanding ecological relationships. This in return will assist in better management of lake water quality. Universiti Kebangsaan Malaysia 2012-08 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/5414/1/01%2520M.sorayya.pdf Sorayya, M and Aishah, and Mohd. Sapiyan, B (2012) Supervised and unsupervised artificial neural networks for analysis of diatom abundance in tropical Putrajaya Lake, Malaysia. Sains Malaysiana, 41 (8). pp. 939-947. ISSN 0126-6039 http://www.ukm.my/jsm
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	Five years of data from 2001 until 2006 of warm unstratified shallow, oligotrophic to mesothropic tropical Putrajaya Lake, Malaysia were used to study pattern discovery and forecasting of the diatom abundance using supervised and unsupervised artificial neural networks. Recurrent artificial neural network (RANN) was used for the supervised artificial neural network and Kohonen Self Organizing Feature Maps (SOM) was used for unsupervised artificial neural network. RANN was applied for forecasting of diatom abundance. The RANN performance was measured in terms of root mean square error (RMSE) and the value reported was 29.12 cell/mL. Classification and clustering by SOM and sensitivity analysis from the RANN were used to reveal the relationship among water temperature, pH, nitrate nitrogen (NO3-N) concentration, chemical oxygen demand (COD) concentration and diatom abundance. The results indicated that the combination of supervised and unsupervised artificial neural network is important not only for forecasting algae abundance but also in reasoning and understanding ecological relationships. This in return will assist in better management of lake water quality.
format	Article
author	Sorayya, M Aishah, Mohd. Sapiyan, B
spellingShingle	Sorayya, M Aishah, Mohd. Sapiyan, B Supervised and unsupervised artificial neural networks for analysis of diatom abundance in tropical Putrajaya Lake, Malaysia
author_facet	Sorayya, M Aishah, Mohd. Sapiyan, B
author_sort	Sorayya, M
title	Supervised and unsupervised artificial neural networks for analysis of diatom abundance in tropical Putrajaya Lake, Malaysia
title_short	Supervised and unsupervised artificial neural networks for analysis of diatom abundance in tropical Putrajaya Lake, Malaysia
title_full	Supervised and unsupervised artificial neural networks for analysis of diatom abundance in tropical Putrajaya Lake, Malaysia
title_fullStr	Supervised and unsupervised artificial neural networks for analysis of diatom abundance in tropical Putrajaya Lake, Malaysia
title_full_unstemmed	Supervised and unsupervised artificial neural networks for analysis of diatom abundance in tropical Putrajaya Lake, Malaysia
title_sort	supervised and unsupervised artificial neural networks for analysis of diatom abundance in tropical putrajaya lake, malaysia
publisher	Universiti Kebangsaan Malaysia
publishDate	2012
url	http://journalarticle.ukm.my/5414/ http://journalarticle.ukm.my/5414/ http://journalarticle.ukm.my/5414/1/01%2520M.sorayya.pdf
first_indexed	2023-09-18T19:44:03Z
last_indexed	2023-09-18T19:44:03Z
_version_	1777405781032828928

Supervised and unsupervised artificial neural networks for analysis of diatom abundance in tropical Putrajaya Lake, Malaysia

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