Genetic programming for analysis and real-time prediction of coastal algal blooms

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@Article{Muttil:2005:EM,
  author =       "Nitin Muttil and Joseph H. W. Lee",
  title =        "Genetic programming for analysis and real-time
                 prediction of coastal algal blooms",
  journal =      "Ecological Modelling",
  year =         "2005",
  volume =       "189",
  number =       "3-4",
  pages =        "363--376",
  month =        "10 " # dec,
  keywords =     "genetic algorithms, genetic programming, Harmful algal
                 blooms, Red tides, Data-driven models, Real-time
                 prediction, Water quality modelling, Hong Kong",
  DOI =          "doi:10.1016/j.ecolmodel.2005.03.018",
  abstract =     "Harmful algal blooms (HAB) have been widely reported
                 and have become a serious environmental problem world
                 wide due to its negative impacts to aquatic ecosystems,
                 fisheries, and human health. A capability to predict
                 the occurrence of algal blooms with an acceptable
                 accuracy and lead-time would clearly be very beneficial
                 to fisheries and environmental management. In this
                 study, we present the first real-time modelling and
                 prediction of algal blooms using a data driven
                 evolutionary algorithm, Genetic Programming (GP). The
                 daily prediction of the algal blooms is carried out at
                 Kat O station in Hong Kong using 3 years of high
                 frequency (two-hourly) chlorophyll fluorescence and
                 related hydro-meteorological and water quality data.
                 The results for the prediction of chlorophyll
                 fluorescence, a measure of algal biomass, are within
                 reasonable accuracy for a lead-time of up to 1 day. The
                 results generally concur with those obtained with
                 artificial neural network. As compared to traditional
                 data-driven models, GP has the advantage of evolving an
                 equation relating input and output variables. A
                 detailed analysis of the results of the GP models shows
                 that GP not only correctly identifies the key input
                 variables in accordance with ecological reasoning, but
                 also demonstrates the relationship between the
                 auto-regressive nature of bloom dynamics and flushing
                 time. This study shows GP to be a viable alternative
                 for algal bloom modelling and prediction; the
                 interpretation of the results is greatly facilitated by
                 the analytical form of the evolved equations.",
}

Genetic Programming entries for Nitin Muttil Joseph Hun-wei Lee

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