%0 Journal Article %1 Kim:2007:EM %A Kim, Dong-Kyun %A Cao, Hongqing %A Jeong, Kwang-Seuk %A Recknagel, Friedrich %A Joo, Gea-Jae %D 2007 %J Ecological Modelling %K Algebraic Evolutionary Machine Microcystis Regulated Rule-based Sensitivity aeruginosa, algorithms, analysis computation, function genetic learning, model, programming, river, %N 1-2 %P 147--156 %R doi:10.1016/j.ecolmodel.2006.03.040 %T Predictive function and rules for population dynamics of Microcystis aeruginosa in the regulated Nakdong River (South Korea), discovered by evolutionary algorithms %V 203 %X Two algorithms of evolutionary computation, an algebraic function model and a rule-based model, were applied for model development with respect to 8 years of limnological data from the lower Nakdong River. The aim of the modelling was to reproduce the abundances of the phytoplankton species, Microcystis aeruginosa, based on physical, chemical and meteorological parameters. The algebraic function model overestimated or underestimated abundance values, but correctly recognised the timing of high abundances. The rule-based model detected not only the timing of algal blooms well but also the magnitude of abundances. Sensitivity analysis indicates that high water temperature influences high abundances of M. aruginosa. In addition, dissolved oxygen, pH, nitrate and phosphate are shown to be explainable in relation to deoxygeneration, carbon dioxide transformation and nutrient limitations.

@article{Kim:2007:EM, abstract = {Two algorithms of evolutionary computation, an algebraic function model and a rule-based model, were applied for model development with respect to 8 years of limnological data from the lower Nakdong River. The aim of the modelling was to reproduce the abundances of the phytoplankton species, Microcystis aeruginosa, based on physical, chemical and meteorological parameters. The algebraic function model overestimated or underestimated abundance values, but correctly recognised the timing of high abundances. The rule-based model detected not only the timing of algal blooms well but also the magnitude of abundances. Sensitivity analysis indicates that high water temperature influences high abundances of M. aruginosa. In addition, dissolved oxygen, pH, nitrate and phosphate are shown to be explainable in relation to deoxygeneration, carbon dioxide transformation and nutrient limitations.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Kim, Dong-Kyun and Cao, Hongqing and Jeong, Kwang-Seuk and Recknagel, Friedrich and Joo, Gea-Jae}, biburl = {https://www.bibsonomy.org/bibtex/2acb7902ef1bde1c4698456cb5b6872fd/brazovayeye}, doi = {doi:10.1016/j.ecolmodel.2006.03.040}, interhash = {a60ff06b6e2f319fd7d54cc2fd68e34d}, intrahash = {acb7902ef1bde1c4698456cb5b6872fd}, journal = {Ecological Modelling}, keywords = {Algebraic Evolutionary Machine Microcystis Regulated Rule-based Sensitivity aeruginosa, algorithms, analysis computation, function genetic learning, model, programming, river,}, month = {24 April}, note = {Special Issue on Ecological Informatics: Biologically-Inspired Machine Learning, 4th Conference of the International Society for Ecological Informatics}, notes = {a Department of Biology, Pusan National University, Jang-Jeon Dong, Gum-Jeong Gu, Busan 609-735, South Korea b School of Earth and Environmental Sciences, University of Adelaide, SA 5005, Australia}, number = {1-2}, pages = {147--156}, timestamp = {2008-06-19T17:43:18.000+0200}, title = {Predictive function and rules for population dynamics of Microcystis aeruginosa in the regulated Nakdong River (South Korea), discovered by evolutionary algorithms}, volume = 203, year = 2007 }