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ESTIMATING POINT-SOURCE IMPACTS ON THE BEAUFORT RIVER USING ARTIFICIAL NEURAL NETWORK MODELS

The Beaufort River is a complex estuarine river system that supports a variety of uses including shellfish grounds, fisheries nursery habitats, shipping access to Port Royal, receiving waters for wastewater effluent, and an 32kilometer section of the Intracoastal Waterway. The river is on the 303(d) list of impaired waters of South Carolina for low dissolved-oxygen concentrations. The Clean Water Act stipulates that a Total Maximum Daily Load must be determined for impaired waters. Artificial neural network (ANN) models and other data mining techniques were applied in the Beaufort River system to quantify the relationships between the time series of four wastewater pointsource discharges and the dissolved-oxygen concentrations recorded at seven real-time gages distributed about the system. The analysis included environmental factors such as water temperature, tides, and rainfall. This paper describes findings of the relationship between one of the point sources and a nearby gage. It was found that the effects of biochemical oxygen demand and ammonia loads on the dissolvedoxygen concentrations vary significantly with water temperature and tidal conditions. Depending on tidal conditions, calculations estimate that at a water temperature of 20° Celsius, a reduction of 100 lbs/day of 5-day biochemical oxygen demand from the point source will increase the dissolvedoxygen concentration at the nearby gage by 0.073 mg/L. The corresponding change in dissolved oxygen relative to 100 lbs/day of NH3 is 0.16 mg/L. KEY TERMS: estuary, dissolved oxygen, point-source loading, neural network models 1 U.S. Geological Survey, 720 Gracern Road, Suite 129, Columbia, SC, 29210, Phone: (803) 750