DEVELOPMENT OF AN EMPIRICAL MODEL OF A COMPLEX, TIDALLY AFFECTED RIVER USING ARTIFICIAL NEURAL NETWORKS

Abstract

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 18-mile reach of the Intracoastal Waterway. The river is on the Section 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. An empirical model was developed to simulate the impact of point-source discharges and rainfall on dissolved-oxygen concentrations in the Beaufort River. The model uses of water level, specific conductance, temperature, and dissolved-oxygen concentration data collected at 15-minute intervals from seven real-time gaging stations and effluent point-source data collected on a weekly basis for a 33month period. The empirical model utilizes data mining techniques, including artificial neural network (ANN) models, to quantify the relations between the time series of three wastewater point-source discharges and the dissolved-oxygen concentrations recorded at seven real-time gages distributed throughout the system. Data mining is a new science that extracts knowledge from large volumes of data, and uses attributes from fields such as computer science, signal processing, advanced statistics, machine learning, and chaos theory. The data mining produced a high-fidelity water-quality model that can predict the impacts that point and non-point source loads have on the dissolved-oxygen concentration throughout the river system. The analysis included environmental factors such as tides, specific conductance, water temperature, and rainfall. The model is comprised of numerous sub-models that are based on ANN models. The data analyses and model provided unique ways to evaluate complex tidal dissolved-oxygen effects from point-source discharges and rainfall. The model executes non-iteratively, making it amenable to very long-term simulation runs of 33 months. The model also included a non-linear, constraint-based numerical optimizer to determine the maximum allowable daily effluent loading without violating the State’s water-quality standard. Insights were garnered from this technical approach that leveraged the full historical record in which assimilative capacity was found to be constantly changing. For example, critical conditions for effluent impacts on dissolved-oxygen concentrations occur during neap tides due to the streamflow characteristics and limited flushing of the system. The predictive model/optimizer allowed for a variety of wastewater treatment plant operating scenarios and regulatory options that can be quickly evaluated. Several 33-month time series of daily loadings were simulated utilizing an optimizer. Frequency distributions of the allowable loading were subsequently generated from the time series of optimal loading. Water- resource managers can use the frequency distribution to help predict the percentage of time water-quality standards may be violated. Model dissemination is facilitated by incorporating the ANN sub-models and point-source optimizers into an Excel spreadsheet application. This paper describes the data collection and analysis, model development and Excel application, pointsource load optimization, and interpretation of model results from this unconventional approach to estuary water-quality modeling and regulatory control.