Development of a framework of automated water quality parameter optimization and its application

This paper presents a methodology and framework for the development of an automated least-squares optimization tool for calibrating water quality parameters in QUAL2E. The method has been applied to estimate the optimal water quality parameters in simulation of stream water quality for the Anyang stream in Korea. The Monte Carlo analysis is used to assess the relative importance of model parameters for water quality constituents. It is found that μ_max and ρ are the most influential parameters for Chlorophyll-a modeling and K ₁ and K ₃ are critical parameters for variation of DO and BOD in the Anyang stream. A computer program for automated parameter calibration has been developed using a nonlinear GRG optimization algorithm. The application framework provides an intuitive and easy-to-use interface and allows visual evaluation of results. According to the simulation results, the automated approach is computationally efficient for evaluation of model parameters and converges on a best fit more rapidly and reliably than a trial and error method. The methodology proposed herein can be extended to other models to obtain the best possible parameter values.