Mining For Ecological Thresholds And
Associations In Cytometric Data:
A Coastal Management Perspective
Author(s)
G. C. Pereira, A. R. Figueiredo & N. F. F. Ebecken
Abstract
Decision-making in coastal waters management is a complex and
interdisciplinary task. Particularly, to find seasonal patterns and ecological
thresholds, which are not always clear in tropical areas. Therefore, the ultimate
in this activity is to gain knowledge about biogenic element, the biological
response, and the selection of indicators which may reveal the trophic status of
the system. Under this scenario, this paper applies Data Mining techniques as an
alternative approach in order to access hidden patterns of in situ flow cytometry
monitoring data. The case studied is the upwelling influenced bay at Cabo Frio
Island (Rio de Janeiro-Brazil). A neural network uses phytoplankton and
bacterial data of real time monitoring as input variables to forecast marine
viruses temporal variability. We also demonstrate that it is possible to access
patterns of planktonic community structure in different water masses within a set
of association rules.
Keywords: knowledge discovery, data mining, pattern recognition,
environmental monitoring, coastal management.
Keywords
knowledge discovery, data mining, pattern recognition,
environmental monitoring, coastal management.
Related Book
Other papers in this volume
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