Intercomparisons between empirical models with data fusion techniques for monitoring water quality in a large lake

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Ni-Bin Chang and Benjamin Vannah",
  booktitle =    "10th IEEE International Conference on Networking,
                 Sensing and Control (ICNSC 2013)",
  title =        "Intercomparisons between empirical models with data
                 fusion techniques for monitoring water quality in a
                 large lake",
  year =         "2013",
  month =        apr,
  pages =        "258--263",
  keywords =     "genetic algorithms, genetic programming, environmental
                 science computing, geophysical image processing, image
                 fusion, image resolution, lakes, learning (artificial
                 intelligence), microorganisms, statistical analysis,
                 water pollution control, water quality, GP model, IDFM
                 technique, Lake Erie, Landsat, blue-green algae, cell
                 growth, cell maintenance, cyanobacteria, data fusion
                 technique, eutrophic condition, hepatotoxin, machine
                 learning technique, microcystin, spatial resolution,
                 statistical index, synthetic image possessing, temporal
                 resolution, water quality monitoring, Data fusion,
                 harmful algal bloom, machine-learning, microcystin,
                 remote sensing, surface reflectance",
  DOI =          "doi:10.1109/ICNSC.2013.6548747",
  abstract =     "Lake Erie has a history of algal blooms, due to
                 eutrophic conditions attributed to urban and
                 agricultural activities. Blue-green algae or
                 cyanobacteria thrive in these eutrophic conditions,
                 since they require little energy for cell maintenance
                 and growth. Microcystis are a type of blue-green algae
                 of particular concern, because they produce
                 microcystin, a potent hepatotoxin. Microcystin not only
                 presents a threat to the ecosystem, but it threatens
                 commercial fishing operations and water treatment
                 plants using the lake as a water source. In this paper,
                 we have proposed an early warning system using
                 Integrated Data Fusion and Machine-learning (IDFM)
                 techniques to determine 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.
                 Analysis of the results through statistical indices
                 confirmed that the Genetic Programming (GP) model has
                 potential accurately estimating microcystin
                 concentrations in the lake (R2 = 0.5699).",
  notes =        "Also known as \cite{6548747}",

Genetic Programming entries for Ni-Bin Chang Benjamin W Vannah