Title

Intercomparisons Between Empirical Models With Data Fusion Techniques For Monitoring Water Quality In A Large Lake

Keywords

Data fusion; harmful algal bloom; machine-learning; microcystin; remote sensing; surface reflectance

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 (R 2 = 0.5699). © 2013 IEEE.

Publication Date

8-14-2013

Publication Title

2013 10th IEEE International Conference on Networking, Sensing and Control, ICNSC 2013

Number of Pages

258-263

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ICNSC.2013.6548747

Socpus ID

84881279437 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84881279437

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