Unravelling and forecasting algal population dynamics in two lakes different in morphometry and eutrophication by neural and evolutionary computation

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@Article{Recknagel:2006:EI,
  author =       "Friedrich Recknagel and Hongqing Cao and 
                 Bomchul Kim and Noriko Takamura and Amber Welk",
  title =        "Unravelling and forecasting algal population dynamics
                 in two lakes different in morphometry and
                 eutrophication by neural and evolutionary computation",
  journal =      "Ecological Informatics",
  year =         "2006",
  volume =       "1",
  number =       "2",
  pages =        "133--151",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, Recurrent
                 supervised artificial neural networks, Non-supervised
                 artificial neural networks, Hybrid evolutionary
                 algorithms, Lake Kasumigaura, Lake Soyang,
                 Cyanobacteria, Diatoms, Time series modelling,
                 Ordination, Clustering, Forecasting",
  doi =          "doi:10.1016/j.ecoinf.2006.02.004",
  abstract =     "Precious ecological information extracted from
                 limnological long-term time series advances the theory
                 on functioning and evolution of freshwater ecosystems.
                 This paper presents results of applications of
                 artificial neural networks (ANN) and evolutionary
                 algorithms (EA) for ordination, clustering, forecasting
                 and rule discovery of complex limnological time-series
                 data of two distinctively different lakes. Ten years of
                 data of the shallow and hypertrophic Lake Kasumigaura
                 (Japan) are used in comparison with 13 years of data of
                 the deep and mesotrophic Lake Soyang (Korea). Results
                 demonstrate the potential that: (1) recurrent
                 supervised ANN and EA facilitate 1-week-ahead
                 forecasting of outbreaks of harmful algae or water
                 quality changes, (2) EA discover explanatory rule sets
                 for timing and abundance of harmful outbreaks algal
                 populations, and (3) non-supervised ANN provide
                 clusters to unravel ecological relationships regarding
                 seasons, water quality ranges and long-term
                 environmental changes.",
  notes =        "a University of Adelaide, School of Earth and
                 Environmental Sciences, Adelaide, 5005, Australia

                 b Kangwon University, Department of Environmental
                 Sciences, Chunchon 200-701, South Korea

                 c National Institute for Environmental Studies, Tsukuba
                 305-0053, Japan",
}

Genetic Programming entries for Friedrich Recknagel Hong-Qing Cao Bomchul Kim Noriko Takamura Amber Welk