210
10.6
Conclusions
K.-S. Jeong . F. Recknagel . G.-1. Joo
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 weIl 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.
Acknowledgements
The authors thank Dr. H. W. Kim of Sunchon National University and Dr. K. Ha
of Pusan National University (PNU) for their efforts in the analyses of plankton
data. We appreciate Mr. S. B. Park and 1. G. Kim for their helps with field
sampling. This study was supported by the Institute of Environmental Technology
and Industry (IETI) (Project No., 01-10-99-01-A-l). This is a contribution No. 22
of Nakdong River Research Programme in Limnology Lab., PNU.
References
Blom N, Gammeltoft S, Brunak S (1999) Sequence and structure-based prediction of
eukaryotic protein phosphorylation sites. N. Mol. Biol., 294: 1351-1362
Bobbin J, Recknagel F (2001) Knowledge discovery for prediction and explanation ofbluegreen algal dynamies in lakes by evolutionary algorithms. Ecol. Modelling 146, 1-3,
253-262
Brosse S, Guegan JF, Tourenq JN, Lek S (1999) The use of artificial neural networks to
assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake.
Ecol. Modelling, 120: 299-311
Bullinaria JA (1997) Modeling reading, spelling, and past tense learning with artificial
neural networks. Brain Lang., 59: 236-266
Burt TP (1992) The Hydrology of Headwater Catchments. In: (Eds) P. Calow and G. E.
Petts. The River Handbook: Hydrological and Ecological Principles. Vol. 1. Blackwell
Scientific Publication, Oxford, 526 pp
10.6
Conclusions
K.-S. Jeong . F. Recknagel . G.-1. Joo
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 weIl 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.
Acknowledgements
The authors thank Dr. H. W. Kim of Sunchon National University and Dr. K. Ha
of Pusan National University (PNU) for their efforts in the analyses of plankton
data. We appreciate Mr. S. B. Park and 1. G. Kim for their helps with field
sampling. This study was supported by the Institute of Environmental Technology
and Industry (IETI) (Project No., 01-10-99-01-A-l). This is a contribution No. 22
of Nakdong River Research Programme in Limnology Lab., PNU.
References
Blom N, Gammeltoft S, Brunak S (1999) Sequence and structure-based prediction of
eukaryotic protein phosphorylation sites. N. Mol. Biol., 294: 1351-1362
Bobbin J, Recknagel F (2001) Knowledge discovery for prediction and explanation ofbluegreen algal dynamies in lakes by evolutionary algorithms. Ecol. Modelling 146, 1-3,
253-262
Brosse S, Guegan JF, Tourenq JN, Lek S (1999) The use of artificial neural networks to
assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake.
Ecol. Modelling, 120: 299-311
Bullinaria JA (1997) Modeling reading, spelling, and past tense learning with artificial
neural networks. Brain Lang., 59: 236-266
Burt TP (1992) The Hydrology of Headwater Catchments. In: (Eds) P. Calow and G. E.
Petts. The River Handbook: Hydrological and Ecological Principles. Vol. 1. Blackwell
Scientific Publication, Oxford, 526 pp