Maximizing Data-Collection Networks by Using Data-Mining Techniques – Case Studies in the Florida Everglades
Abstract:The sustainable development of water resources requires the understanding of the interaction between natural processes of ecological systems and the social and economic drivers of societal systems. Often, there are limited data to analyze anthropogenic influences on natural systems to quantify the interaction between ecological and societal systems. Frequently a comprehensive database is not available for a system but rather there are disparate databases that are temporally and spatially limited. This paper describes the technical approach and results of two case studies where data mining was applied to large-scale environmental monitoring networks in the Florida Everglades, and disparate databases were integrated to provide a more comprehensive database of ecological and societal interactions. The first case study demonstrates how short-term and long-term datasets to can be integrated to provide long-term hydrologic time-series histories at critical nesting and foraging habitats. The second case study demonstrates how “dynamic” (time series) and “static” data, which characterize site-to-site differences that change little over time, can be integrated to predict water depths at ungaged locations.
Document Type: Research Article
Publication date: January 1, 2008
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