基于SWMM-贝叶斯耦合方法的排水管网污染溯源

Pollution source tracing in sewer networks based on a Bayesian-SWMM coupled approach

为解决雨水管道中未经处理的污水直接排放造成的一系列水污染问题和排水管网环境复杂性导致的反问题不确定性问题,基于暴雨洪水管理模型(SWMM)模拟的排水管网水质变化,采用MatSWMM工具箱构建了基于贝叶斯-马尔可夫链蒙特卡罗法的SWMM-贝叶斯溯源模型,估算每个潜在污染源的位置、排放量和排放时间的概率分布。实例验证结果表明:在推理单污染源单个未知参数的情况下,SWMM-贝叶斯溯源模型可准确判断未知参数的真实值;当单污染源3个参数均未知时,由于存在多种组合性,溯源准确度将显著降低,只能判断出近似范围,但可以通过增加适当的水质监测点,提高SWMM-贝叶斯溯源模型的准确性和效率;对于最大污染源为2个的情况,SWMM-贝叶斯溯源模型容易陷于局部最优解,通过利用似然函数对迭代过程的初始值进行择优处理的方法改进溯源模型,可以有效解决局部最优解的问题。

In order to address the problem of directly discharging of untreated sewage from storm pipes causing a range of water pollution problems and the uncertainty problem of source inference due to the complexity of drainage system conditions,the storm water management model(SWMM)was employed to simulate the variation of water quality within the drainage system.A SWMM-Bayesian traceability model based on the Bayesian-MCMC(Monte Carlo Markov Chain)method was constructed using the MatSWMM toolbox to predict the probability distributions of the location of each potential source,the extent of discharge,and the time of discharge.Example validation results show that in the case of reasoning about a single unknown parameter of a single pollution source,the SWMM-Bayesian traceability model can accurately determine the true value of the unknown parameter.When all three parameters of a single pollution source are unknown,due to the existence of a variety of combinatorial properties,the traceability accuracy will be reduced significantly,and can only be judged to be in the approximate range,but it can be increased by adding the appropriate water quality monitoring points,to improve the accuracy and efficiency of SWMM-Bayesian traceability model.For the case that the maximum number of pollution sources is 2,the SWMM-Bayesian traceability model is easy to be trapped in the local optimal solution,and the improvement of the traceability model by using the likelihood function to optimize the initial value of the iterative process can effectively solve the problem of local optimal solution.