Comparative Data Fusion between Genetic Programing and Neural Network Models for Remote Sensing Images of Water Quality Monitoring

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@InProceedings{Chang:2013:SMC,
  author =       "Ni-Bin Chang and Benjamin Vannah",
  title =        "Comparative Data Fusion between Genetic Programing and
                 Neural Network Models for Remote Sensing Images of
                 Water Quality Monitoring",
  booktitle =    "IEEE International Conference on Systems, Man, and
                 Cybernetics (SMC 2013)",
  year =         "2013",
  month =        oct,
  pages =        "1046--1051",
  keywords =     "genetic algorithms, genetic programming, Data fusion,
                 machine-learning, remote sensing, surface reflectance,
                 microcystin, harmful algal bloom",
  DOI =          "doi:10.1109/SMC.2013.182",
  size =         "6 pages",
  abstract =     "Historically, algal blooms have proliferated
                 throughout Western Lake Erie as a result of eutrophic
                 conditions caused by urban growth and agricultural
                 activities. Of great concern is the blue-green algae
                 Microcystis that thrives in eutrophic conditions and
                 generates microcystin, a powerful hepatotoxin.
                 Microcystin poses a threat to the delicate ecosystem of
                 Lake Erie, and it threatens commercial fishing
                 operations and water treatment plants using the lake as
                 a water source. Integrated Data Fusion and
                 Machine-learning (IDFM) is an early warning system
                 proposed by this paper for the prediction of
                 microcystin concentrations and distribution by
                 measuring the surface reflectance of the water body
                 using satellite sensors. The fine spatial resolution of
                 Landsat is fused with the high temporal resolution of
                 MODIS to create a synthetic image possessing both high
                 temporal and spatial resolution. As a demonstration,
                 the spatiotemporal distribution of microcystin within
                 western Lake Erie is reconstructed using the band data
                 from the fused products and applied machine-learning
                 techniques. The performance of Artificial Neural
                 Networks (ANN) and Genetic Programming (GP) are
                 compared and tested against traditional two-band model
                 regression techniques. It was found that the GP model
                 performed slightly better at predicting microcystin
                 with an R-squared value of 0.6020 compared to 0.5277
                 for ANN.",
  notes =        "Also known as \cite{6721935}",
}

Genetic Programming entries for Ni-Bin Chang Benjamin W Vannah

Citations