Harmful algal blooms (HAB) have been widely reported
and have become a serious environmental problem world
wide due to its negative impacts to aquatic ecosystems,
fisheries, and human health. A capability to predict
the occurrence of algal blooms with an acceptable
accuracy and lead-time would clearly be very beneficial
to fisheries and environmental management. In this
study, we present the first real-time modelling and
prediction of algal blooms using a data driven
evolutionary algorithm, Genetic Programming (GP). The
daily prediction of the algal blooms is carried out at
Kat O station in Hong Kong using 3 years of high
frequency (two-hourly) chlorophyll fluorescence and
related hydro-meteorological and water quality data.
The results for the prediction of chlorophyll
fluorescence, a measure of algal biomass, are within
reasonable accuracy for a lead-time of up to 1 day. The
results generally concur with those obtained with
artificial neural network. As compared to traditional
data-driven models, GP has the advantage of evolving an
equation relating input and output variables. A
detailed analysis of the results of the GP models shows
that GP not only correctly identifies the key input
variables in accordance with ecological reasoning, but
also demonstrates the relationship between the
auto-regressive nature of bloom dynamics and flushing
time. This study shows GP to be a viable alternative
for algal bloom modelling and prediction; the
interpretation of the results is greatly facilitated by
the analytical form of the evolved equations.
%0 Journal Article
%1 Muttil:2005:EM
%A Muttil, Nitin
%A Lee, Joseph H. W.
%D 2005
%J Ecological Modelling
%K Data-driven Harmful Hong Kong Real-time Red Water algal algorithms, blooms, genetic modelling, models, prediction, programming, quality tides,
%N 3-4
%P 363--376
%R doi:10.1016/j.ecolmodel.2005.03.018
%T Genetic programming for analysis and real-time
prediction of coastal algal blooms
%V 189
%X Harmful algal blooms (HAB) have been widely reported
and have become a serious environmental problem world
wide due to its negative impacts to aquatic ecosystems,
fisheries, and human health. A capability to predict
the occurrence of algal blooms with an acceptable
accuracy and lead-time would clearly be very beneficial
to fisheries and environmental management. In this
study, we present the first real-time modelling and
prediction of algal blooms using a data driven
evolutionary algorithm, Genetic Programming (GP). The
daily prediction of the algal blooms is carried out at
Kat O station in Hong Kong using 3 years of high
frequency (two-hourly) chlorophyll fluorescence and
related hydro-meteorological and water quality data.
The results for the prediction of chlorophyll
fluorescence, a measure of algal biomass, are within
reasonable accuracy for a lead-time of up to 1 day. The
results generally concur with those obtained with
artificial neural network. As compared to traditional
data-driven models, GP has the advantage of evolving an
equation relating input and output variables. A
detailed analysis of the results of the GP models shows
that GP not only correctly identifies the key input
variables in accordance with ecological reasoning, but
also demonstrates the relationship between the
auto-regressive nature of bloom dynamics and flushing
time. This study shows GP to be a viable alternative
for algal bloom modelling and prediction; the
interpretation of the results is greatly facilitated by
the analytical form of the evolved equations.
@article{Muttil:2005:EM,
abstract = {Harmful algal blooms (HAB) have been widely reported
and have become a serious environmental problem world
wide due to its negative impacts to aquatic ecosystems,
fisheries, and human health. A capability to predict
the occurrence of algal blooms with an acceptable
accuracy and lead-time would clearly be very beneficial
to fisheries and environmental management. In this
study, we present the first real-time modelling and
prediction of algal blooms using a data driven
evolutionary algorithm, Genetic Programming (GP). The
daily prediction of the algal blooms is carried out at
Kat O station in Hong Kong using 3 years of high
frequency (two-hourly) chlorophyll fluorescence and
related hydro-meteorological and water quality data.
The results for the prediction of chlorophyll
fluorescence, a measure of algal biomass, are within
reasonable accuracy for a lead-time of up to 1 day. The
results generally concur with those obtained with
artificial neural network. As compared to traditional
data-driven models, GP has the advantage of evolving an
equation relating input and output variables. A
detailed analysis of the results of the GP models shows
that GP not only correctly identifies the key input
variables in accordance with ecological reasoning, but
also demonstrates the relationship between the
auto-regressive nature of bloom dynamics and flushing
time. This study shows GP to be a viable alternative
for algal bloom modelling and prediction; the
interpretation of the results is greatly facilitated by
the analytical form of the evolved equations.},
added-at = {2008-06-19T17:46:40.000+0200},
author = {Muttil, Nitin and Lee, Joseph H. W.},
biburl = {https://www.bibsonomy.org/bibtex/2b2e958fc75e17d33adaf4d260af4a477/brazovayeye},
doi = {doi:10.1016/j.ecolmodel.2005.03.018},
interhash = {8b420ff5df8a43bd9d647aa811c3955c},
intrahash = {b2e958fc75e17d33adaf4d260af4a477},
journal = {Ecological Modelling},
keywords = {Data-driven Harmful Hong Kong Real-time Red Water algal algorithms, blooms, genetic modelling, models, prediction, programming, quality tides,},
month = {10 December},
number = {3-4},
pages = {363--376},
timestamp = {2008-06-19T17:47:54.000+0200},
title = {Genetic programming for analysis and real-time
prediction of coastal algal blooms},
volume = 189,
year = 2005
}