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

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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