Improvement of remote monitoring on water quality in a subtropical reservoir by incorporating grammatical evolution with parallel genetic algorithms into satellite imagery

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@Article{Chen2008296,
  author =       "Li Chen and Chih-Hung Tan and Shuh-Ji Kao and 
                 Tai-Sheng Wang",
  title =        "Improvement of remote monitoring on water quality in a
                 subtropical reservoir by incorporating grammatical
                 evolution with parallel genetic algorithms into
                 satellite imagery",
  journal =      "Water Research",
  volume =       "42",
  number =       "1-2",
  pages =        "296--306",
  year =         "2008",
  ISSN =         "0043-1354",
  DOI =          "doi:10.1016/j.watres.2007.07.014",
  URL =          "http://www.sciencedirect.com/science/article/B6V73-4P7FS78-1/2/1cc0a607d7b67fe51a5f0d27a2c9d0fc",
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 evolution, Parallel genetic algorithm, Water quality
                 monitoring, Chlorophyll-a, Remote-sensed imagery",
  abstract =     "Parallel GEGA was constructed by incorporating
                 grammatical evolution (GE) into the parallel genetic
                 algorithm (GA) to improve reservoir water quality
                 monitoring based on remote sensing images. A cruise was
                 conducted to ground-truth chlorophyll-a (Chl-a)
                 concentration longitudinally along the Feitsui
                 Reservoir, the primary water supply for Taipei City in
                 Taiwan. Empirical functions with multiple spectral
                 parameters from the Landsat 7 Enhanced Thematic Mapper
                 (ETM+) data were constructed. The GE, an evolutionary
                 automatic programming type system, automatically
                 discovers complex nonlinear mathematical relationships
                 among observed Chl-a concentrations and remote-sensed
                 imageries. A GA was used afterward with GE to optimize
                 the appropriate function type. Various parallel
                 subpopulations were processed to enhance search
                 efficiency during the optimization procedure with GA.
                 Compared with a traditional linear multiple regression
                 (LMR), the performance of parallel GEGA was found to be
                 better than that of the traditional LMR model with
                 lower estimating errors.",
}

Genetic Programming entries for Li Chen Chih-Hung Tan Shuh-Ji Kao Tai-Sheng Wang

Citations