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

Diserahkan: 29 Jun 2011 / Diterima: 31 Januari 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 daripada RANN 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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*Pengarang surat-menyurat; email: pian@um.edu.my