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 Dx and Dy 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. PRACTITIONER POINTS: Bayesian-based water quality model parameter calibration method is proposed and posterior probability distribution was sampled using the MCMC algorithm. Parameter calibration can be completed more efficiently by setting an optimized initial value for the MCMC. As a result, efficient and accurate parameter calibration of water quality models was achieved. This method is widely applicable to various models, and the calibration speed depends on the calculation speed of the model.
Keywords: Bayesian inference; Markov chain Monte Carlo; parameter calibration; water quality model.
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