Transforming Large Databases Into Critical Knowledge Using Data Mining– Three Case Studies in South Carolina and Georgia

Data mining is an emerging field that addresses the issue of converting large databases into knowledge. Data mining methods come from different technical fields such as signal processing, statistics, and artificial intelligence. Data mining employs methods for maximizing the information content of data, determining which variables have the strongest relationships to problems of interest, and developing models that predict future outcomes. Data mining is used extensively in financial services, banking, advertising, manufacturing, and e-commerce to classify the behaviors of organizations and individuals, and predict future outcomes. This paper describes the results of three case studies where data mining, including artificial neural network models, has been applied to large-scale environmental issues in South Carolina and Georgia. For the Beaufort River, South Carolina, dissolved-oxygen models were developed and used for determining Total Maximum Daily Load of allowable point-source effluent loading to the Beaufort River. For the Savannah River estuary, models were developed to simulated pore-water salinity and used to determine the potential impacts of deepening the Savannah Harbor on upstream freshwater tidal marshes. For the Pee Dee River in South Carolina, models were developed to determine the minimum streamflow required to protect municipal intakes from seawater inundation along the Grand Strand of South Carolina. In the three studies, the models were able to convincingly reproduce historical behaviors and generate alternative scenarios of interest. To make the results of the studies directly available to all stakeholders, user-friendly decision support systems were developed as a spreadsheet application that integrates the historical database, models, user controls, streaming graphics, and simulation output.