Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms

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@Article{chan:2007:WR,
  author =       "Wai Sum Chan and Friedrich Recknagel and 
                 Hongqing Cao and Ho-Dong Park",
  title =        "Elucidation and short-term forecasting of microcystin
                 concentrations in Lake Suwa (Japan) by means of
                 artificial neural networks and evolutionary
                 algorithms",
  journal =      "Water Research",
  year =         "2007",
  volume =       "41",
  number =       "10",
  pages =        "2247--2255",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, Lake Suwa,
                 Microcystis, Microcystin, Ordination, Clustering,
                 Forecasting, Explanation",
  DOI =          "doi:10.1016/j.watres.2007.02.001",
  abstract =     "Non-supervised artificial neural networks (ANN) and
                 hybrid evolutionary algorithms (EA) were applied to
                 analyse and model 12 years of limnological time-series
                 data of the shallow hypertrophic Lake Suwa in Japan.
                 The results have improved understanding of
                 relationships between changing microcystin
                 concentrations, Microcystis species abundances and
                 annual rainfall intensity. The data analysis by
                 non-supervised ANN revealed that total Microcystis
                 abundance and extra-cellular microcystin concentrations
                 in typical dry years are much higher than those in
                 typical wet years. It also showed that high microcystin
                 concentrations in dry years coincided with the
                 dominance of the toxic Microcystis viridis whilst in
                 typical wet years non-toxic Microcystis ichthyoblabe
                 were dominant. Hybrid EA were used to discover rule
                 sets to explain and forecast the occurrence of high
                 microcystin concentrations in relation to water quality
                 and climate conditions. The results facilitated early
                 warning by 3-days-ahead forecasting of microcystin
                 concentrations based on limnological and meteorological
                 input data, achieving an r2=0.74 for testing.",
  notes =        "Wai Sum (Grace) Chan

                 a School of Earth and Environmental Sciences,
                 University of Adelaide, Adelaide 5005, Australia

                 b Cooperative Research Centre for Water Quality and
                 Treatment, Salisbury 5108, Australia

                 c Department of Environmental Sciences, Shinshu
                 University, Matsumoto 390-8621, Japan",
}

Genetic Programming entries for Wai Sum Chan Friedrich Recknagel Hong-Qing Cao Ho-Dong Park

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