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

Created by W.Langdon from gp-bibliography.bib Revision:1.8203

@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",
• month = jan,
• keywords = "genetic algorithms, genetic programming, Grammatical evolution, Parallel genetic algorithm, Water quality monitoring, Chlorophyll-a, Remote-sensed imagery",
• ISSN = "0043-1354",
• DOI = "doi:10.1016/j.watres.2007.07.014",
• broken = "http://www.sciencedirect.com/science/article/B6V73-4P7FS78-1/2/1cc0a607d7b67fe51a5f0d27a2c9d0fc",
• size = "11 pages",
• 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