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Prediction and Elucidation of Population Dynamics of the Blue-green Algae Microcystis aeruginosa and the Diatom Stephanodiscus hantzschii in the Nakdong River-Reservoir System (South Korea) by a Recurrent Artificial Neural Network

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Ecological Informatics

12.6 Conclusions

Artificial neural networks were applied to the prediction and elucidation of two bloom forming algal species in the Nakdong river-reservoir system. The lower Nakdong River, which has characteristics of both rivers and reservoirs, represents a complicated system for algal bloom modeling. Yet, RNN proved capable not only to predict the distinct seasonal abundance and succession of Microcystis aeruginosa and Stephanodiscus hantzschii but elucidate key driving variables by means of sensitivity analyses. Findings of the sensitivity analysis corresponded very well with existing theories on the ecology of these two algae species.

This study yields promising results for the application of machine learning to complex ecosystems such as regulated rivers. It encourages inter-disciplinary research between ecologists, modelers and computer scientists in the newly emerging area of ecological informatics in order to better understand and predict ecological phenomena at different levels of organization.

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Authors and Affiliations

  1. Department of Biology, Pusan National University, Jang-Jeon Dong, Gum-Jeong Gu, Busan, 609-735, Korea

    K. -S. Jeong & G. -J. Joo

  2. School of Earth and Environmental Sciences, University of Adelaide, Adelaide, 5005, Australia

    F. Recknagel

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  1. K. -S. Jeong
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  1. School of Earth and Environmental Sciences, The University of Adelaide, 5005, Australia

    Friedrich Recknagel

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Jeong, K.S., Recknagel, F., Joo, G.J. (2006). Prediction and Elucidation of Population Dynamics of the Blue-green Algae Microcystis aeruginosa and the Diatom Stephanodiscus hantzschii in the Nakdong River-Reservoir System (South Korea) by a Recurrent Artificial Neural Network. In: Recknagel, F. (eds) Ecological Informatics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28426-5_12

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