Neural network and genetic programming for modelling coastal algal blooms

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{oai:inderscience.com:11208,

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