Department of Mathematics, Zhengzhou University, Zhengzhou, Henan, PR China.
J Contam Hydrol. 2010 Jan 15;111(1-4):1-12. doi: 10.1016/j.jconhyd.2009.10.004. Epub 2009 Nov 5.
Site uncertainties significantly influence groundwater flow and contaminant transport predictions. Aleatoric and epistemic uncertainty are both identified in site characterization and represented using proper uncertainty theories. When one theory best represents one parameter whereas a different theory may be more suitable for another parameter, the hybrid propagation of aleatoric (random) and epistemic (nonrandom) uncertainties will occur. The computational challenges of joint propagation of aleatoric and epistemic uncertainty through groundwater flow and contaminant transport models are significant. A fuzzy-stochastic nonlinear model was developed in this paper to incorporate these two types of uncertain site information and reduce the computational cost. The results show that (1) the computational cost using the nonlinear model is reduced compared with that of using the sparse grid algorithm and Monte Carlo methods; (2) the uncertainty of hydraulic conductivity (K) significantly influences the water head and solute distribution at the observation wells compared to other uncertain parameters, such as the storage coefficient and the distribution coefficient (K(d)); and (3) the combination of multiple uncertain parameters substantially affects the simulation results. Neglecting site uncertainties may lead to unrealistic predictions.
场地不确定性会显著影响地下水流动和污染物运移预测。在场地特征描述中会识别出并使用适当的不确定性理论来表示随机性不确定性和认知性不确定性。当一种理论最能代表一个参数，而另一种理论可能更适合另一个参数时，就会发生随机性（随机）和认知性（非随机）不确定性的混合传播。通过地下水流动和污染物运移模型联合传播随机性和认知性不确定性的计算挑战非常大。本文开发了一个模糊随机非线性模型，以纳入这两种类型的场地不确定性信息并降低计算成本。结果表明：(1) 与稀疏网格算法和蒙特卡罗方法相比，使用非线性模型可以降低计算成本；(2) 与其他不确定参数（如储水系数和分配系数 (K(d)) 相比，水力传导率 (K) 的不确定性对观测井的水头和溶质分布有显著影响；(3) 多个不确定参数的组合会显著影响模拟结果。忽略场地不确定性可能会导致不切实际的预测。
