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:1.7630

@InProceedings{Chang:2013:ieeeICNSCerie,

• 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

Citations