Title

Compartive Data Fusion Between Genetic Programing And Nueral Network Models For Remote Sensing Images Of Water Quality Monitoring

Keywords

Data fusion; Harmful algal bloom; Machine-learning; Microcystin; Remote sensing; Surface reflectance

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 R2 value of 0.6020 compared to 0.5277 for ANN. © 2013 IEEE.

Publication Date

12-1-2013

Publication Title

Proceedings - 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013

Number of Pages

1046-1051

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/SMC.2013.182

Socpus ID

84893556956 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84893556956

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