Extending Morris method for qualitative global sensitivity analysis of models with dependent inputs

    Research output: Contribution to journalArticle

    Abstract

    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.

    Original languageEnglish (US)
    Pages (from-to)28-39
    Number of pages12
    JournalReliability Engineering and System Safety
    Volume162
    DOIs
    StatePublished - Jun 1 2017

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    Sensitivity analysis
    Screening
    Experiments

    Keywords

    • Dependent contributions
    • Dependent inputs
    • Global sensitivity analysis
    • Independent contributions
    • Screening
    • Variance-based sensitivity indexes

    ASJC Scopus subject areas

    • Safety, Risk, Reliability and Quality
    • Industrial and Manufacturing Engineering

    Cite this

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    title = "Extending Morris method for qualitative global sensitivity analysis of models with dependent inputs",
    abstract = "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.",
    keywords = "Dependent contributions, Dependent inputs, Global sensitivity analysis, Independent contributions, Screening, Variance-based sensitivity indexes",
    author = "Qiao Ge and Monica Menendez",
    year = "2017",
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    N2 - 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.

    AB - 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.

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    KW - Dependent inputs

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    KW - Screening

    KW - Variance-based sensitivity indexes

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