基于局部-全局混合自适应替代模型的地下水污染源反演识别

Groundwater pollution sources inversion based on local-global hybrid adaptive surrogate model

为了提高地下水污染源反演识别的精度及计算效率,综合运用最优解准则及交叉验证-维诺图准则,提出了局部-全局混合自适应替代模型.将局部-全局混合自适应替代模型与遗传算法结合,应用于地下水污染源反演算例中,并与局部自适应替代模型及全局自适应替代模型进行对比.结果表明:基于局部-全局混合自适应替代模型的遗传算法得到的污染源反演结果精度最高且计算耗时最少,其反演结果与实际污染源特征基本一致,最大相对误差仅为3.51%.证明了所提出的局部-全局混合自适应替代模型在提高地下水污染源反演精度及计算效率方面的有效性.

A local-global hybrid adaptive surrogate model was proposed based on optimal solution criterion and cross validation-Voronoi(CV-Voronoi)criterion to improve the inversion accuracy and computational efficiency of groundwater pollution source.The local-global hybrid adaptive surrogate model combined with genetic algorithm was applied to groundwater pollution sources inversion case.The inversion results were compared with those of the local and the global adaptive surrogate model.The comparison results reveal that the local-global hybrid adaptive surrogate model combined genetic algorithm had the highest inversion accuracy and the lowest computational cost.The pollution sources inversion results can identify the actual pollution source characteristics,and the maximum relative error was only 3.51%.The results of the paper prove the robustness of the proposed local-global hybrid adaptive surrogate model in improving the accuracy and computational efficiency of groundwater pollution sources inversion.