Neural network and genetic programming for modelling coastal algal blooms
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{oai:inderscience.com:11208,
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title = "Neural network and genetic programming for modelling
coastal algal blooms",
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author = "Nitin Muttil and Kwok-Wing Chau",
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journal = "International Journal of Environment and Pollution",
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year = "2006",
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volume = "28",
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pages = "223--238",
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number = "3/4",
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month = "6 " # nov,
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publisher = "Inderscience Publishers",
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bibsource = "OAI-PMH server at www.inderscience.com",
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language = "eng",
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oai = "oai:inderscience.com:11208",
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relation = "ISSN online: 1741-5101 ISSN print: 0957-4352 DOI:
10.1504/06.11208",
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rights = "Inderscience Copyright",
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source = "IJEP (2006), Vol 28 Issue 3/4, pp 223 - 238",
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keywords = "genetic algorithms, genetic programming, harmful algal
blooms, machine learning techniques, artificial neural
networks, water quality modelling, Hong Kong, algal
biomass, environmental pollution, simulation",
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ISSN = "1741-5101",
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URL = "http://www.inderscience.com/link.php?id=11208",
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DOI = "doi:10.1504/IJEP.2006.011208",
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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
long-term 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.",
- }
Genetic Programming entries for
Nitin Muttil
Kwok-Wing Chau
Citations