Multi-parameter identification of a two-dimensional water-quality model based on the Nelder–Mead Simplex algorithm

Abstract

To solve the multi-parameter identification problem of a two-dimensional river water-quality model, a new parameter identification method based on the Nelder–Mead Simplex algorithm coupled with the alternating direction implicit method has been constructed to determine hydraulic and water-quality parameters such as the longitudinal dispersion coefficient, the transverse mixing coefficient, and the pollutant degradation coefficient. Moreover, the influences of observation noise, observation location, and sampling frequency on the identified parameters were discussed for the given model. The method was validated using three numerical cases (two steady state and one dynamic), and one field experiment. The computational results indicated that the model provided good identification precision and showed good anti-noise capability. The longitudinal distribution of observed points made it possible to identify the contributions of the degradation coefficient K and the transverse distribution to the identification of the transverse dispersion coefficient Ey. Sampling frequency has a strong influence on the accuracy of the identified parameters. Generally, the higher the sampling frequency, the higher will be the accuracy obtained, but the convergence rate may be slow and the computational time lengthy. Therefore, when dealing with practical problems, a reasonable balance should be sought between the amount of calculation required and the parameter estimation accuracy.