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Sains Malaysiana 41(8)(2012):
939–947
Supervised and
Unsupervised Artificial Neural Networks for Analysis of
Diatom Abundance in
Tropical Putrajaya Lake, Malaysia
(Rangkaian Neural Buatan Diselia dan Tanpa Penyeliaan untuk Analisis Kelimpahan
Diatom di Tasik Tropika Putrajaya,
Malaysia)
M. Sorayya & S. Aishah
Institute
of Biological Sciences (ISB), University of Malaya, 50603 Kuala Lumpur,
Malaysia
B. Mohd. Sapiyan*
Faculty
of Science Computer and Information Technology, University of Malaya,
50603
Kuala Lumpur, Malaysia
Received:
29 June 2011 / Accepted: 31 January 2012
ABSTRACT
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.
Keywords: Diatom;
forecasting; recurrent artificial neural network; self organizing maps
ABSTRAK
Data selama lima tahun dari 2001 hingga 2006 bagi tasik tropika yang cetek dan panas, berstatus oligotrof ke mesotropi iaitu Tasik Putrajaya, Malaysia telah digunakan untuk mengkaji penemuan corak dan ramalan kuantiti diatom menggunakan rangkaian neural buatan yang diselia dan tidak diselia. Rangkaian neural buatan berulang (RANN) telah digunakan untuk rangkaian neural buatan diselia dan peta atur sendiri Kohonen (SOM) telah digunakan untuk rangkaian neural buatan tanpa pengawasan.RANN telah digunakan untuk ramalan kuantiti diatom. Prestasi RANN diukur daripada ralat min punca kuasa dua (RMSE) dan nilai yang dilaporkan adalah 29.12 sel/mL. Pengelasan dan kelompok oleh SOM dan analisis kepekaan daripadaRANN digunakan untuk mendedahkan hubungan antara suhu air, pH, kepekatan nitrogen nitrat (NO3-N), keperluan oksigen kimia (COD) dan kuantiti diatom. Keputusan menunjukkan bahawa gabungan rangkaian diselia dan tidak diselia neural buatan adalah penting bukan sahaja untuk ramalan pertumbuhan alga tetapi juga dalam analisis dan pemahaman hubungan ekologi. Ini akan membantu dalam pengurusan yang lebih baik bagi kualiti air tasik.
Kata kunci: Diatom; peta susun sendiri; ramalan; rangkaian neural buatan berulang
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*Corresponding author; email: pian@um.edu.my
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