A sensitivity analysis of the PAWN sensitivity index

Puy, Arnald; Piano, Samuele Lo; Saltelli, Andrea

doi:10.1016/j.envsoft.2020.104679

Cited by 34 publications

(23 citation statements)

References 31 publications

(96 reference statements)

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“…Furthermore, differences in the relative values of the median and maximum statistics used to define PAWN indices highlight relevant aspects for further analysis, such as the presence of specific input ranges that may lead to outliers on certain impacts. Nonetheless, the PAWN indices should ideally be used in a "meta-sensitivity analysis" framework (Puy et al 2020) to test the robustness of these indices to parameters of the method, such as the number of conditioning intervals and sampling type.…”

Section: Discussionmentioning

confidence: 99%

Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks

Jaxa-Rozen

Pratiwi

Trutnevyte

2021

Int J Life Cycle Assess

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Purpose Global sensitivity analysis increasingly replaces manual sensitivity analysis in life cycle assessment (LCA). Variance-based global sensitivity analysis identifies influential uncertain model input parameters by estimating so-called Sobol indices that represent each parameter’s contribution to the variance in model output. However, this technique can potentially be unreliable when analyzing non-normal model outputs, and it does not inform analysts about specific values of the model input or output that may be decision-relevant. We demonstrate three emerging methods that build on variance-based global sensitivity analysis and that can provide new insights on uncertainty in typical LCA applications that present non-normal output distributions, trade-offs between environmental impacts, and interactions between model inputs. Methods To identify influential model inputs, trade-offs, and decision-relevant interactions, we implement techniques for distribution-based global sensitivity analysis (PAWN technique), spectral clustering, and scenario discovery (patient rule induction method: PRIM). We choose these techniques because they are applicable with generic Monte Carlo sampling and common LCA software. We compare these techniques with variance-based Sobol indices, using a previously published LCA case study of geothermal heating networks. We assess eight environmental impacts under uncertainty for three design alternatives, spanning different geothermal production temperatures and heating network configurations. Results In the application case on geothermal heating networks, PAWN distribution-based sensitivity indices generally identify influential model parameters consistently with Sobol indices. However, some discrepancies highlight the potentially misleading interpretation of Sobol indices on the non-normal distributions obtained in our analysis, where variance may not meaningfully describe uncertainty. Spectral clustering highlights groups of model results that present different trade-offs between environmental impacts. Compared to second-order Sobol interaction indices, PRIM then provides more precise information regarding the combinations of input values associated with these different groups of calculated impacts. PAWN indices, spectral clustering, and PRIM have a computational advantage because they yield stable results at relatively small sample sizes (n = 12,000), unlike Sobol indices (n = 100,000 for second-order indices). Conclusions We recommend adding these new techniques to global sensitivity analysis in LCA as they give more precise as well as additional insights on uncertainty regardless of the distribution of the model outputs. PAWN distribution-based global sensitivity analysis provides a computationally efficient assessment of input sensitivities as compared to variance-based global sensitivity analysis. The combination of clustering and scenario discovery enables analysts to precisely identify combinations of input parameters or uncertainties associated with different outcomes of environmental impacts.

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Section: Discussionmentioning

confidence: 99%

Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks

Jaxa-Rozen

Pratiwi

Trutnevyte

2021

Int J Life Cycle Assess

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“…In this section, we formulate the benchmarking parameters as random variables and assess how the performance of estimators is dependent on them by performing a SA. In essence, this is a sensitivity analysis of sensitivity analyses [42], and a natural extension of a similar uncertainty analysis in a recent work by Becker [21]. The use of global SA tools to better understand the properties of estimators can give insights into how estimators behave in different scenarios that are not available through analytical approaches.…”

Section: Assessment Of the Uncertainties In The Benchmarking Parametersmentioning

confidence: 99%

A Comprehensive Comparison of Total-Order Estimators for Global Sensitivity Analysis

Puy

Becker

Piano

et al. 2022

Int. J. UncertaintyQuantification

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Sensitivity analysis helps identify which model inputs convey the most uncertainty to the model output. One of the most authoritative measures in global sensitivity analysis is the Sobol' total-order index, which can be computed with several different estimators. Although previous comparisons exist, it is hard to know which estimator performs best since the results are contingent on the benchmark setting defined by the analyst (the sampling method, the distribution of the model inputs, the number of model runs, the test function or model and its dimensionality, the weight of higher order effects, or the performance measure selected). Here we compare several total-order estimators in an eight-dimension hypercube, where these benchmark parameters are treated as random parameters. This arrangement significantly relaxes the dependency of the results on the benchmark design. We observe that the most accurate estimators are from Razavi and Gupta, Jansen, or Janon/Monod for factor prioritization, and from Jansen, Janon/Monod, or Azzini and Rosati for approaching the "true" total-order indices. The rest lag considerably behind. Our work helps analysts navigate myriad total-order formulae by reducing the uncertainty in the selection of the most appropriate estimator.

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“…One possible option is to combine VISCOUS with multiple-response emulators of Liu et al (2019), which integrates the copula functions with multiple-response Gaussian process emulator. In addition, since the GMCM used in VISCOUS has a set of design parameters, a "sensitivity analysis of sensitivity analysis" (Puy et al, 2020) should be tried in future studies to investigate VISCOUS sensitivity to its own design parameters.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

VISCOUS: A Variance‐Based Sensitivity Analysis Using Copulas for Efficient Identification of Dominant Hydrological Processes

Sheikholeslami

Gharari

Papalexiou

et al. 2021

Water Resources Research

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Introduction On the High Computational Cost Incurred by the Sampling-Based Global Sensitivity AnalysisWith the rapid increase in computational capability, an increasing number of distributed and semi-distributed process-based hydrologic models have been introduced to simulate the quantity and quality of water on a range of spatiotemporal scales. The ever-growing complexity of these models is driven by the

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A sensitivity analysis of the PAWN sensitivity index

Cited by 34 publications

References 31 publications

Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks

Variance-based global sensitivity analysis and beyond in life cycle assessment: an application to geothermal heating networks

A Comprehensive Comparison of Total-Order Estimators for Global Sensitivity Analysis

VISCOUS: A Variance‐Based Sensitivity Analysis Using Copulas for Efficient Identification of Dominant Hydrological Processes

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