Comparing Physics-Based and Neural Network Models for Simulating Salinity, Temperature, and Dissolved Oxygen in a Complex, Tidally Affected River Basin

Abstract

The U.S. Geological Survey participated in an evaluation of using neural networks for modeling estuarine systems, and selected physics-based, dynamic one-dimensional flow and water-quality models of the Cooper and Wando Rivers for comparison. The results showed that the neural network models were more accurate at simulating salinity, temperature, and dissolved oxygen of this very complex estuarine system than physics-based models. They were also far less costly to develop and can be easily integrated into commercial software, such as spreadsheets, and real-time data acquisition and control systems. However, the physics-based models may provide a better understanding of the system’s behavior and provide a better methodology for extrapolating to environmental conditions that are not manifest in the historical data.