Integrated data fusion and mining for monitoring water quality in Lake Eri
The frequencies of occurrence of Harmful Algal Bloom (HAB) events in Lake Erie and elsewhere may be tied to an integrated response of climate change impact (temperature change) and anthropogenic disturbance (changing nutrient cycling). Blue-green algae or cyanobacteria are photosynthetic bacteria that require little energy for cell maintenance and growth, giving them a distinct advantage over competition. Urban growth and agricultural production have caused an influx of nutrients into Lake Erie, leading to eutrophic zones. These conditions result in the formation of algal blooms, some of which are toxic due to the presence of Microcystis (a cyanobacteria), which produces the hepatotoxin microcystin. Microcystis has a unique advantage over its competition as a result of the invasive zebra mussel population that filters algae out of the water column except for the toxic Microcystis. The toxin threatens human health and the ecosystem, and it is a concern for water treatment plants using the lake water as a tap water source. This paper demonstrates the prototype of a near real-time early warning system using Integrated Data Fusion and Mining (IDFM) techniques to determine spatiotemporal microcystin concentrations and 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 the Moderate Resolution Imaging Spectroradiometer to create a synthetic image possessing algorithm producing images with 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 model has potential to accurately estimating microcystin concentrations in the lake, which is better than all current 2-band and 3-band models and other computational intelligence models.
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