Applying artificial neural network theory to exploring diatom abundance at tropical Putrajaya Lake, Malaysia

Abstract

This article explores the relationship between diatom abundance and water quality variables in tropical Putrajaya Lake based on limnological data collected from 2001 to 2006, using supervised and unsupervised artificial neural networks (ANNs). Recurrent artificial neural network (RANN) was used for the supervised ANNs and Kohonen Self Organizing Feature Maps (SOMs) for the unsupervised ANNs. The RANN was developed for the prediction of diatom abundance using variables selected by sensitivity analysis (water temperature, pH, dissolved oxygen, and turbidity). The RANN model performance was measured using root mean squared error (19.0 cell/mL) and the r-value (0.7). SOM was used in this study for classification and clustering of diatom abundance in relation to selected water quality variables and was validated using a sensitivity curve of diatom abundance over the selected variable range generated from RANN. SOM has been employed in this study for pattern discovery of diatom abundance at Putrajaya Lake. The extracted patterns of diatom abundance in terms of propositional IF. . .else rules were tested and yielded an accuracy rate of 87%. © 2012 Taylor & Francis.