%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 }