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Predictive function and rules for population dynamics of Microcystis aeruginosa in the regulated Nakdong River (South Korea), discovered by evolutionary algorithms

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  • Kim, Dong-Kyun
  • Cao, Hongqing
  • Jeong, Kwang-Seuk
  • Recknagel, Friedrich
  • Joo, Gea-Jae

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 recognized 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.

Suggested Citation

  • Kim, Dong-Kyun & Cao, Hongqing & Jeong, Kwang-Seuk & Recknagel, Friedrich & Joo, Gea-Jae, 2007. "Predictive function and rules for population dynamics of Microcystis aeruginosa in the regulated Nakdong River (South Korea), discovered by evolutionary algorithms," Ecological Modelling, Elsevier, vol. 203(1), pages 147-156.
    • Handle: RePEc:eee:ecomod:v:203:y:2007:i:1:p:147-156
    DOI: 10.1016/j.ecolmodel.2006.03.040
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    References listed on IDEAS

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    1. Neely, Christopher J. & Weller, Paul A., 2001. "Technical analysis and central bank intervention," Journal of International Money and Finance, Elsevier, vol. 20(7), pages 949-970, December.
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    1. Recknagel, Friedrich & Ostrovsky, Ilia & Cao, Hongqing & Zohary, Tamar & Zhang, Xiaoqing, 2013. "Ecological relationships, thresholds and time-lags determining phytoplankton community dynamics of Lake Kinneret, Israel elucidated by evolutionary computation and wavelets," Ecological Modelling, Elsevier, vol. 255(C), pages 70-86.
    2. Cao, Hongqing & Recknagel, Friedrich & Orr, Philip T., 2013. "Enhanced functionality of the redesigned hybrid evolutionary algorithm HEA demonstrated by predictive modelling of algal growth in the Wivenhoe Reservoir, Queensland (Australia)," Ecological Modelling, Elsevier, vol. 252(C), pages 32-43.
    3. Kim, MinHyeok & Park, Namyong & (Bob) McKay, R.I. & Shin, Haisoo & Lee, Yun-Geun & Jeong, Kwang-Seuk & Kim, Dong-Kyun, 2014. "Improvement of complex and refractory ecological models: Riverine water quality modelling using evolutionary computation," Ecological Modelling, Elsevier, vol. 291(C), pages 205-217.
    4. Shin, Jiyoun & Kim, Kyung-Ho & Lee, Kang-Kun & Kim, Hyoung-Soo, 2010. "Assessing temperature of riverbank filtrate water for geothermal energy utilization," Energy, Elsevier, vol. 35(6), pages 2430-2439.
    5. Cao, Hongqing & Recknagel, Friedrich & Bartkow, Michael, 2016. "Spatially-explicit forecasting of cyanobacteria assemblages in freshwater lakes by multi-objective hybrid evolutionary algorithms," Ecological Modelling, Elsevier, vol. 342(C), pages 97-112.
    6. Jeong, Kwang-Seuk & Jang, Ji-Deok & Kim, Dong-Kyun & Joo, Gea-Jae, 2011. "Waterfowls habitat modeling: Simulation of nest site selection for the migratory Little Tern (Sterna albifrons) in the Nakdong estuary," Ecological Modelling, Elsevier, vol. 222(17), pages 3149-3156.
    7. Zhang, Xiaoqing & Recknagel, Friedrich & Chen, Qiuwen & Cao, Hongqing & Li, Ruonan, 2015. "Spatially-explicit modelling and forecasting of cyanobacteria growth in Lake Taihu by evolutionary computation," Ecological Modelling, Elsevier, vol. 306(C), pages 216-225.

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