Extending Morris method for qualitative global sensitivity analysis of models with dependent inputs
Extending Morris method for qualitative global sensitivity analysis of models with dependent inputs
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DOI：
10.1016/j.ress.2017.01.010
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发表时间：
2017-06-01
影响因子：
8.1
通讯作者：
Menendez, Monica
中科院分区：
文献类型：
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作者：
Ge, Qiao;Menendez, Monica
全局灵敏度分析(GSA)可以帮助建模人员更好地理解模型并管理不确定性。然而，当模型本身相当复杂时，特别是当模型输入之间存在相关性时，直接进行定量GSA可能是困难的，甚至是不可行的。本文提出了一种筛选模型输入的非参数方法。它扩展了经典的初等效应(即Morris)方法，该方法被广泛用于筛选独立输入，从而能够筛选相依模型输入。通过三个数值实验对该方法的性能进行了测试，并与基于方差的广义最小二乘法的结果进行了比较，结果表明，该方法能够正确地从具有多个独立和相关输入的复杂模型中识别有影响和无影响的输入。此外，与基于方差的GSA方法相比，该方法只需要少量的模型运行，同时保持了较好的筛选精度。因此，它可以作为一个实用的工具，用于高维和计算昂贵的依赖输入的模型的初始GSA。
Global Sensitivity Analysis (GSA) can help modelers to better understand the model and manage the uncertainty. However, when the model itself is rather sophisticated, especially when dependence exists among model inputs, it could be difficult or even unfeasible to perform quantitative GSA directly. In this paper, a non parametric approach is proposed for screening model inputs. It extends the classic Elementary Effects (i.e., Morris) method, which is widely used for screening independent inputs, to enable the screening of dependent model inputs. The performance of the proposed method is tested with three numerical experiments, and the results are cross-compared with those from the variance-based GSA.It is found that the proposed method can properly identify the influential and non-influential inputs from a complex model with several independent and dependent inputs. Furthermore, compared with the variance based GSA, the proposed screening method only needs a few model runs, while the screening accuracy is well maintained. Therefore, it can be regarded as a practical tool for the initial GSA of high dimensional and computationally expensive models with dependent inputs.