基于参数不确定性的地下水污染治理多目标管理模型  

A multi-objective simulation-optimization model for optimal design of groundwater remediation systems under uncertainty

作　　者：杨蕴[1,2] 吴剑锋[1] 于军[2] 林锦[3] 施小清[1] 吴吉春[1] 

机构地区：[1]南京大学地球科学与工程学院水科学系,南京210093 [2]国土资源部地裂缝地质灾害重点实验室,江苏省地质调查研究院,南京210018 [3]南京水利科学研究院,南京210029

出　　处：《环境科学学报》2013年第7期2059-2067,共9页Acta Scientiae Circumstantiae

基　　金：国家重点基础研究发展计划项目(No.2010CB428803);国家自然科学基金资助项目(No.41072175,40902069,41030746)~~

摘　　要：引入随机Pareto控制排序和随机小生境技术,提出基于参数随机变化的改进小生境Pareto遗传算法,用于求解不确定性条件下地下水污染治理多目标管理模型.同时,利用顺序高斯条件模拟的蒙特卡罗方法,结合不确定性分析和风险评估,分析不同渗透系数条件点数对污染物运移结果不确定性和污染风险评价的影响.最后将该方法应用于一个考虑渗透系数为随机变量的二维地下水污染修复算例中.结果分析表明,该方法可为地下水污染治理提供变异性较小的Pareto管理策略,是一种稳定可靠的多目标随机优化方法.The design of a robust and reliable groundwater remediation system encounters major difficulties owing to the inherent uncertainty of hydrogeological parameters. Based on the commonly used deterministic groundwater multi-objective optimization methods, a probabilistic improved niched Pareto genetic algorithm （PINPGA） is proposed for this purpose. The PINPGA uses two techniques including probabilistic Pareto domination ranking and probabilistic niche technique to find Pareto optimal solutions of groundwater remediation systems under uncertainty. Also, the performance of the proposed algorithm is evaluated through a synthetic pump-and-treat （PAT） groundwater remediation system under a random lognormal distribution of hydraulic conductivity （K） field. At first, the Sequential Gaussian Simulation （SGSIM） is used to generate conditional InK realizations based on the sampled conditioning data acquired by the field test. Then Monte Carlo simulation is applied to address uncertainty analysis and risk assessment of contaminant transport fate associated with different numbers of conditional InK points. Compared with the existing improved niched Pareto genetic algorithm （INPGA） with a simple averaging approach, the proposed PINPGA with a probabilistic and small sample size （ as few as 5 ） of InK realizations can find Pareto optimal solutions with lower variability and higher reliability, leading to a robust decision-making.

关 键 词：地下水污染风险评估 不确定性分析 条件模拟 随机多目标优化 改进小生境Pareto遗传算法 

分 类 号：P641.2[天文地球—地质矿产勘探] X32[天文地球—地质学]