Zhang Kejiang, Li Hua, Achari Gopal
Department of Civil Engineering, University of Calgary, Calgary, AB, Canada T2N 1N4.
J Contam Hydrol. 2009 Apr 15;106(1-2):73-82. doi: 10.1016/j.jconhyd.2009.01.003. Epub 2009 Jan 24.
Site variabilities and uncertainties in data and information lead to significant spread in results of groundwater flow and contaminant transport models. A framework for hybrid propagation of random uncertainties represented by probability theory; nonrandom uncertainties represented by fuzzy set theory; and site variabilities represented by geostatistics was developed in this research. A case study was provided to explain the computational algorithm. The methodology presented here can be applied to complex environments where there are site variabilities as well as uncertainties of different kinds. The algorithm is suited when uncertainties in some variables may be best represented as fuzzy numbers whereas in others as probability distributions and both form part of the same governing equation.
数据和信息中的场地变异性与不确定性导致地下水流和污染物运移模型结果出现显著差异。本研究建立了一个框架，用于混合传播由概率论表示的随机不确定性、由模糊集理论表示的非随机不确定性以及由地质统计学表示的场地变异性。通过一个案例研究来解释计算算法。本文提出的方法可应用于存在场地变异性以及不同类型不确定性的复杂环境。当某些变量中的不确定性可能最好表示为模糊数，而其他变量中的不确定性表示为概率分布，且两者都构成同一控制方程的一部分时，该算法适用。
