Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/1133
Title: Neural network and genetic programming for modelling coastal algal blooms
Authors: Muttil, Nitin
Chau, Kwok-wing
Subjects: Harmful algal blooms
Machine learning techniques
Artificial neural networks
Genetic programming
Water quality modelling
Algal blooms
Hong Kong
Issue Date: 2006
Publisher: Inderscience
Source: International journal of environment and pollution, 2006, v. 28, no. 3/4, p. 223-238.
Abstract: In the recent past, machine learning (ML) techniques such as artificial neural networks (ANN) have been increasingly used to model algal bloom dynamics. In the present paper, along with ANN, we select genetic programming (GP) for modelling and prediction of algal blooms in Tolo Harbour, Hong Kong. The study of the weights of the trained ANN and also the GP-evolved equations shows that they correctly identify the ecologically significant variables. Analysis of various ANN and GP scenarios indicates that good predictions of longterm trends in algal biomass can be obtained using only chlorophyll-a as input. The results indicate that the use of biweekly data can simulate long-term trends of algal biomass reasonably well, but it is not ideally suited to give short-term algal bloom predictions.
Rights: Copyright © 2006 Inderscience Enterprises Ltd. The journal web page at: http://www.inderscience.com/browse/index.php?journalID=9.
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/1133
DOI: 10.1504/IJEP.2006.011208
ISSN: 0957-4352 (print)
1741-5101 (online)
Appears in Collections:CEE Journal/Magazine Articles

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