Machine-learning paradigms for selecting ecologically significant input variables

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Muttil:2007:EAAI,
  author =       "Nitin Muttil and Kwok-Wing Chau",
  title =        "Machine-learning paradigms for selecting ecologically
                 significant input variables",
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "2007",
  volume =       "20",
  number =       "6",
  pages =        "735--744",
  month =        sep,
  note =         "Viewpoint",
  keywords =     "genetic algorithms, genetic programming, Harmful algal
                 blooms, Red tides, Machine-learning techniques,
                 Data-driven models, Artificial neural networks, Water
                 quality modelling, Tolo Harbour, Hong Kong",
  ISSN =         "0952-1976",
  DOI =          "doi:10.1016/j.engappai.2006.11.016",
  size =         "10 pages",
  abstract =     "Harmful algal blooms, which are considered a serious
                 environmental problem nowadays, occur in coastal waters
                 in many parts of the world. They cause acute ecological
                 damage and ensuing economic losses, due to fish kills
                 and shellfish poisoning as well as public health
                 threats posed by toxic blooms. Recently, data-driven
                 models including machine-learning (ML) techniques have
                 been employed to mimic dynamics of algal blooms. One of
                 the most important steps in the application of a ML
                 technique is the selection of significant model input
                 variables. In the present paper, we use two extensively
                 used ML techniques, artificial neural networks (ANN)
                 and genetic programming (GP) for selecting the
                 significant input variables. The efficacy of these
                 techniques is first demonstrated on a test problem with
                 known dependence and then they are applied to a
                 real-world case study of water quality data from Tolo
                 Harbour, Hong Kong. These ML techniques overcome some
                 of the limitations of the currently used techniques for
                 input variable selection, a review of which is also
                 presented. The interpretation of the weights of the
                 trained ANN and the GP evolved equations demonstrate
                 their ability to identify the ecologically significant
                 variables precisely. The significant variables
                 suggested by the ML techniques also indicate
                 chlorophyll-a (Chl-a) itself to be the most significant
                 input in predicting the algal blooms, suggesting an
                 auto-regressive nature or persistence in the algal
                 bloom dynamics, which may be related to the long
                 flushing time in the semi-enclosed coastal waters. The
                 study also confirms the previous understanding that the
                 algal blooms in coastal waters of Hong Kong often occur
                 with a life cycle of the order of 1-2 weeks.",
}

Genetic Programming entries for Nitin Muttil Kwok-Wing Chau

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