The characterization of contaminant transport in subsurface environments is a pre-requisite for sustainable groundwater use and management. Various analytical and numerical models are generally utilized for such characterization. Analytical models are used for solving simple and idealized pollution transport problems, while numerical models deal with real-world pollution transport simulations. However, in situations where any of the aquifer parameters (such as geological properties, boundary conditions, initial conditions, etc.) are not explicitly defined, use of these models becomes redundant. Simulation of pollution transport under such scenarios becomes challenging. To tackle such problems, researchers generally make use of artificial neural network (ANN) models. Existing literature review reveals that feed-forward backpropagation (FFBNN) models are the most commonly used training method while the applicability of other ANN models has not been appropriately explored. In this study, various ANN models, encompassing supervised and unsupervised neural networks, like, cascade-forward backpropagation (CFBNN), FFBNN, radial basis function (RBFNN), exact radial basis function (ERBFNN) and generalized regression (GRNN), have been developed, and their performances are compared for transport simulation of a conservative pollutant in a two-dimensional hypothetical aquifer. The models reported have pollution injection rates and locations of the sources as input data and the pollution concentrations measured in some water supply wells (or monitoring wells) are considered as target data. Stability of the developed models has been validated by sensitivity analyses. This study reveals that alternative ANN models (other than the ones already reported in literature) can be reliable for simulation of pollutant transport in groundwater systems.

表征地下环境中污染物的运移是可持续利用和管理地下水的先决条件。通常将各种分析模型和数值模型用于这种表征。分析模型用于解决简单而理想的污染迁移问题，而数值模型则用于处理现实世界中的污染迁移模拟。但是，在未明确定义任何含水层参数（例如地质属性，边界条件，初始条件等）的情况下，使用这些模型将变得多余。在这种情况下模拟污染的运输变得充满挑战。为了解决这些问题，研究人员通常利用人工神经网络（ANN）模型。现有文献综述表明，前馈反向传播（FFBNN）模型是最常用的训练方法，而其他ANN模型的适用性尚未得到适当探讨。在这项研究中，已经建立了包括监督和非监督神经网络的各种ANN模型，例如级联前向传播（CFBNN），FFBNN，径向基函数（RBFNN），精确径向基函数（ERBFNN）和广义回归（GRNN）。在二维假想含水层中比较保守的污染物的运移，并比较了它们的性能。报告的模型将污染注入率和污染源位置作为输入数据，并将在某些供水井（或监测井）中测得的污染浓度视为目标数据。灵敏度分析已验证了所开发模型的稳定性。这项研究表明，替代的ANN模型（文献中已经报道过的模型除外）对于模拟地下水系统中的污染物迁移是可靠的。