MAROS 是首个基于Windows 平台利用Microsoft Access 和Microsoft Excel 开发出来的用于地下水污染监测网优化设计的决策支持系统软件。该软件通过统计分析监测区域有关数据在时空上的相关分析结果，来确定监测网在时间上的取样频率和空间上的分布密度，从而保证以最省的监测费用获得地下水污染物随时间和空间变化的最全面信息。本文简单地介绍了MAROS 的设计原理和模块组成，并对该软件的优缺点进行了评述。

This study evaluates and compares two methodologies, Monte Carlo simple genetic algorithm (MCSGA) and noisy genetic algorithm (NGA), for cost-effective sampling network design in the presence of uncertainties in the hydraulic conductivity (K) field. Both methodologies couple a genetic algorithm (GA) with a numerical flow and transport simulator and a global plume estimator to identify the optimal sampling network for contaminant plume monitoring. The MCSGA approach yields one optimal design each for a large number of realizations generated to represent the uncertain K-field. A composite design is developed on the basis of those potential monitoring wells that are most frequently selected by the individual designs for different K-field realizations. The NGA approach relies on a much smaller sample of K-field realizations and incorporates the average of objective functions associated with all K-field realizations directly into the GA operators, leading to a single optimal design. The efficacy of the MCSGA-based composite design and the NGA-based optimal design is assessed by applying them to 1000 realizations of the K-field and evaluating the relative errors of global mass and higher moments between the plume interpolated from a sampling network and that output by the transport model without any interpolation. For the synthetic application examined in this study, the optimal sampling network obtained using NGA achieves a potential cost savings of 45% while keeping the global mass and higher moment estimation errors comparable to those errors obtained using MCSGA. The results of this study indicate that NGA can be used as a useful surrogate of MCSGA for cost-effective sampling network design under uncertainty. Compared with MCSGA, NGA reduces the optimization runtime by a factor of 6.5.

随机水文地质学方法，较传统的确定性方法而言，是解决非均质含水层中水流和溶质运移问题的一种更为合理的手段。据以往研究，假设渗透系数场遵循对数正态分布，利用直接傅立叶变换方法来生成渗透系数随机场。应用基于随机理论的蒙特卡罗方法，来研究渗透系数的空间变异性对污染物运移结果的影响。实例研究表明，污染物在含水层中运移过程中污染羽的展布范围（二阶矩） 随着渗透系数空间变异方差的增大而扩大，而污染羽在空间上的质心位置（一阶矩） 基本不受方差的影响，仅取决于渗透系数随机场的均值大小。另外还分析了污染羽在各点的浓度变化方差和变异系数分别随渗透系数变异方差的变化状况。

长期过量开采地下水所引发的地面沉降是一个复杂的非线性变化过程，对其进行数值模拟时，如不考虑沉降发展过程中模型参数的非线性变化，会给预测带来很大误差。本文依据苏锡常地区实际资料，通过典型钻孔土力学试验，确定出土体在不同荷载条件下的弹塑性和蠕变特征方程以及土体渗透系数随孔隙度变化的关系式。在此基础上建立了研究区三维变系数地下水流模型及考虑土体垂向不同变化特征（弹塑变或蠕变） 的一维区域沉降模型，并采用多尺度有限单元法对模型进行了真正意义上的耦合求解。

分别将禁忌搜索和遗传算法与地下水流模型MODFLOW 和地下水溶质运移模型MT3DMS相耦合，并将其应用于求解地下水资源优化管理模型。在概述两种智能算法基本原理和地下水管理模型组成的基础上，结合两个理想的应用实例，从优化结果和计算效率两个方面对禁忌搜索和遗传算法进行了对比分析。在两个实例中，禁忌搜索分别以高于遗传算法10 倍和27 倍的计算效率得到了减少抽水流量约160m3/d和节约治理成本约47万元的治理方案。结果表明，禁忌搜索在求解地下水管理模型中具有较好的应用前景。