Bayesian-based calibration for water quality model parameters

To improve the efficiency and accuracy of water quality model parameter calibration and avoid local optima and the phenomenon in which different parameters have the same effect, this paper proposed a novel Bayesian-based water quality model parameter calibration method. Using Bayesian inference, the parameter calibration problem was converted into a posterior probability function sampling problem, which was sampled using the Markov Chain Monte Carlo algorithm. The convergence speed of the calibration was further improved by setting the optimized initial sampling value. The influences of the initial sampling value, Markov chain length, and proposal distribution form on the calibration effect were evaluated using four specific cases. The results indicate that (1) the mean relative error (MRE) of the parameter calibration results of this method is less than 10%, with the calibration MRE of D-x and D-y being 5.3% and 8.3%, respectively; (2) when the parameter sensitivity is low, the calibration effect of this method is relatively poor, with a calibration MRE of 46% for k; (3) the parameter calibration can be completed more efficiently by setting an optimized initial value for the MCMC, choosing a reasonable Markov chain length and a suitable proposal distribution form.