基于改进Bayesian-MCMC的突发水污染事件预测模型参数率定方法

Forecast model parameters calibration method for sudden water pollution accidents based on improved Bayesian-Markov chain Monte Carlo

预测模型是科学制定应急处置措施的基础.为快速准确地构建突发水污染事件预测模型,将预测模型参数的率定问题视为贝叶斯估计问题,并根据有限差分方法和贝叶斯推理得到参数的后验概率密度函数,再通过改进的Metropolis-Hastings抽样方法得到较为合理的参数值.以发生在某明渠段的突发水污染事件为例,分析讨论等容量控制非均匀流和非等容量控制非均匀流两种情景下不同观测噪声对参数率定值的影响,并与由贝叶斯-马尔科夫链蒙特卡罗方法得到的参数值和真实值进行对比.结果表明:改进Bayesian-MCMC方法在计算精度、适用性和抗噪声等方面优于贝叶斯-马尔科夫链蒙特卡罗方法,能较好地率定模型参数,并为构建突发水污染事件预测模型提供了新思路.

The prediction models are the basis for scientific formulation of emergency disposal and rescue measures. In order to quickly and accurately construct the forecasting model of sudden water pollution accidents, this paper regards the problem as the Bayesian estimation problem and obtains the posterior probability density function of the model parameters according to the finite difference method and Bayesian inference. Then, by using the improved MetropolisHastings sampling method, more reasonable prediction model parameters are obtained. Taking the sudden water pollution event in a certain open channel as an example, the effects of different observation noises on the parameters calibration results are discussed under the two scenarios of non-uniform flow control with non-uniform flow and non-equal capacity control, and compared with the parameter and true value of the Bayesian-Markov chain Monte Carlo method. The results show that, the improved Bayesian-Markov chain Monte Carlo method can give a better parameter value, more application and has a good anti-noise, which can provide a new approach to build the forecast model of sudden water pollution accidents.