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.
%0 Journal Article
%1 oai:inderscience.com:11208
%A Muttil, Nitin
%A Chau, Kwok-Wing
%D 2006
%I Inderscience Publishers
%J International Journal of Environment and Pollution
%K Hong Kong, algal algorithms, artificial biomass, blooms, environmental genetic harmful learning machine modelling, networks, neural pollution, programming, quality simulation techniques, water
%N 3/4
%P 223--238
%R doi:10.1504/IJEP.2006.011208
%T Neural network and genetic programming for modelling
coastal algal blooms
%U http://www.inderscience.com/link.php?id=11208
%V 28
%X 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.
@article{oai:inderscience.com:11208,
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.},
added-at = {2008-06-19T17:46:40.000+0200},
author = {Muttil, Nitin and Chau, Kwok-Wing},
bibsource = {OAI-PMH server at www.inderscience.com},
biburl = {https://www.bibsonomy.org/bibtex/2a5747b319fa794e3aa9fb8ddfc17439b/brazovayeye},
doi = {doi:10.1504/IJEP.2006.011208},
interhash = {cc49a75fba9606b714acffa02ec94f4e},
intrahash = {a5747b319fa794e3aa9fb8ddfc17439b},
issn = {1741-5101},
journal = {International Journal of Environment and Pollution},
keywords = {Hong Kong, algal algorithms, artificial biomass, blooms, environmental genetic harmful learning machine modelling, networks, neural pollution, programming, quality simulation techniques, water},
language = {eng},
month = {6 November},
number = {3/4},
oai = {oai:inderscience.com:11208},
pages = {223--238},
publisher = {Inderscience Publishers},
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},
timestamp = {2008-06-19T17:47:55.000+0200},
title = {Neural network and genetic programming for modelling
coastal algal blooms},
url = {http://www.inderscience.com/link.php?id=11208},
volume = 28,
year = 2006
}