Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes

A genetic algorithm (GA) was combined with artificial neural networks (ANN), designated as neuro-genetic algorithm (NGA) in this study, to determine the effective number of nodes and optimal activated functions (FAs) in an ANN structure. Developed NGA was applied to predict Chlorophyll-a (Chl-a) con...

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Main Authors:	Lee, G., Bae, J., Lee, S., Jang, M., Park, H.
Format:	Article
Published:	Taylor & Francis 2016
Subjects:	TA Engineering (General). Civil engineering (General) TD Environmental technology. Sanitary engineering
Online Access:	http://dx.doi.org/10.1080/19443994.2016.1190107 http://dx.doi.org/10.1080/19443994.2016.1190107

id	um-17757
recordtype	eprints
spelling	um-177572017-09-08T02:45:59Z Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes Lee, G. Bae, J. Lee, S. Jang, M. Park, H. TA Engineering (General). Civil engineering (General) TD Environmental technology. Sanitary engineering A genetic algorithm (GA) was combined with artificial neural networks (ANN), designated as neuro-genetic algorithm (NGA) in this study, to determine the effective number of nodes and optimal activated functions (FAs) in an ANN structure. Developed NGA was applied to predict Chlorophyll-a (Chl-a) concentrations in one-month increments in Lakes used as drinking water sources. Correlation analysis was used to setup input parameters. A simulation was conducted for four study sites with the most serious Chl-a problems in South Korea. Results from correlation analysis have indicated that phosphate phosphorus (PO4-P) and electrical conductivity showed high correlation with Chl-a, a factor not often considered in other studies. As the results of prediction of one-month forward Chl-a concentration, NGA showed high accuracy, with averaged determination coefficients of 0.89 and 0.84 in training and testing period, respectively. Double hidden layers showed better performance than a single hidden layer, while a logistic sigmoid function was frequently selected by the genetic algorithm in hidden layers in comparison with linear and hyperbolic tangent function. Practical uses for NGA in proactive water quality management are also discussed in this study. Taylor & Francis 2016 Article PeerReviewed http://dx.doi.org/10.1080/19443994.2016.1190107 Lee, G.; Bae, J.; Lee, S.; Jang, M.; Park, H. (2016) Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes. Desalination and Water Treatment <http://eprints.um.edu.my/view/publication/Desalination_and_Water_Treatment.html>, 57 (55). pp. 26783-26791. ISSN 1944-3994 http://eprints.um.edu.my/17757/
repository_type	Digital Repository
institution_category	Local University
institution	University Malaya
building	UM Research Repository
collection	Online Access
topic	TA Engineering (General). Civil engineering (General) TD Environmental technology. Sanitary engineering
spellingShingle	TA Engineering (General). Civil engineering (General) TD Environmental technology. Sanitary engineering Lee, G. Bae, J. Lee, S. Jang, M. Park, H. Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes
description	A genetic algorithm (GA) was combined with artificial neural networks (ANN), designated as neuro-genetic algorithm (NGA) in this study, to determine the effective number of nodes and optimal activated functions (FAs) in an ANN structure. Developed NGA was applied to predict Chlorophyll-a (Chl-a) concentrations in one-month increments in Lakes used as drinking water sources. Correlation analysis was used to setup input parameters. A simulation was conducted for four study sites with the most serious Chl-a problems in South Korea. Results from correlation analysis have indicated that phosphate phosphorus (PO4-P) and electrical conductivity showed high correlation with Chl-a, a factor not often considered in other studies. As the results of prediction of one-month forward Chl-a concentration, NGA showed high accuracy, with averaged determination coefficients of 0.89 and 0.84 in training and testing period, respectively. Double hidden layers showed better performance than a single hidden layer, while a logistic sigmoid function was frequently selected by the genetic algorithm in hidden layers in comparison with linear and hyperbolic tangent function. Practical uses for NGA in proactive water quality management are also discussed in this study.
format	Article
author	Lee, G. Bae, J. Lee, S. Jang, M. Park, H.
author_facet	Lee, G. Bae, J. Lee, S. Jang, M. Park, H.
author_sort	Lee, G.
title	Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes
title_short	Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes
title_full	Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes
title_fullStr	Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes
title_full_unstemmed	Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes
title_sort	monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in lakes
publisher	Taylor & Francis
publishDate	2016
url	http://dx.doi.org/10.1080/19443994.2016.1190107 http://dx.doi.org/10.1080/19443994.2016.1190107
first_indexed	2018-09-06T06:44:45Z
last_indexed	2018-09-06T06:44:45Z
_version_	1610839527483506688

Monthly chlorophyll-a prediction using neuro-genetic algorithm for water quality management in Lakes

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