基于模拟-优化方法的地下水污染源溯源辨识

以抚顺市某煤矸石堆放场为研究区，根据研究区的实际条件建立地下水污染质运移模拟模型，预测地下水污染质未来时空变化特征.基于正演预报结果构建了假想例子，应用模拟-优化方法对地下水污染源源强及场地的渗透系数进行反演识别.为减小优化模型反复调用模拟模型所产生的计算负荷，分别采用Kriging方法和BP神经网络方法建立了模拟模型的替代模型.最后运用模拟退火法求解优化模型，得到反演识别结果.研究表明：应用Kriging方法建立的替代模型输出结果的平均相对误差为0.3%；应用BP神经网络方法建立替代模型的输出结果平均相对误差为1.5%，应用两种替代模型对污染源源强识别的相对误差均小于0.5%，对场地两个参数分区渗透系数识别的相对误差均不大于5%.综上，应用Kriging方法建立的替代模型精度高于BP神经网络方法，利用基于两种替代模型的模拟-优化方法对污染源源强和渗透系数进行同步识别精度可以满足实际需求，是有效可行的.

Inverse Identification of groundwater pollution source based on simulation-optimization approach

A coal gangue pile in Fushun City was selected as the study area, and the groundwater numerical simulation model for this area was established based on physical condition. After that, we applied the model in predicting possible spatial and temporal variation of groundwater pollutant in research area. Based on forward modelling, a hypothetical example was designed to explore the application of simulation-optimization method and simultaneously carried out inverse identification of groundwater pollution sources and model parameters. The Kriging method and BP Neural Network method were proposed to establish a surrogate model of the groundwater numerical model so as to reduce the computational load caused by the repeated invocation of the simulation model by the optimization model, and the surrogate model was then integrated to simulation-optimization model where simulated annealing method was used. The study reached the following conclusions:As the surrogate model of the simulation model established by Kriging method, the mean relative error of the output concentration is 0.3%, by contrast with 1.5% with BP Neural Network method. The identification error of the release intensity of pollution source with two methods are both below 0.5%, and the identification error of hydraulic conductivity both partitions is no larger than 5%. Above all, the accuracy of surrogate model established by Kriging method is higher than BP neural network method. It is proved that the inverse identification by using the simulation-optimization method based on two surrogate models is effective and accurate.