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Global sensitivity analysis with aggregated Shapley effects, application to avalanche hazard assessment

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  • Heredia, María Belén
  • Prieur, Clémentine
  • Eckert, Nicolas

Abstract

Dynamic models are simplified representations of some real-world entities that change over time. They are essential analytical tools with significant applications, e.g., in environmental and social sciences. Due to physical constraints applied on the outputs, it happens that input parameters are confined to a non-rectangular domain. In order to perform sensitivity analysis in this setting, we introduce the notion of aggregated Shapley effects and we propose an algorithm to estimate them with associated bootstrap confidence intervals. Our procedure is applied to analyze the sensitivity of an avalanche flow dynamic model from an input/output sample obtained by considering only input combinations leading to avalanche events that are both realistic and of interest for risk purposes. More precisely, we analyze the sensitivity in two different settings: (i) little knowledge on the input parameter probability distribution, and (ii) well-calibrated input parameter distribution. This leads insightful results regarding avalanche dynamics and potential related hazard, which demonstrate the usefulness of our approach for practical problems.

Suggested Citation

  • Heredia, María Belén & Prieur, Clémentine & Eckert, Nicolas, 2022. "Global sensitivity analysis with aggregated Shapley effects, application to avalanche hazard assessment," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000904
    DOI: 10.1016/j.ress.2022.108420
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    References listed on IDEAS

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    1. Lamboni, Matieyendou & Monod, Hervé & Makowski, David, 2011. "Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 450-459.
    2. Alexanderian, Alen & Gremaud, Pierre A. & Smith, Ralph C., 2020. "Variance-based sensitivity analysis for time-dependent processes," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    3. Goda, Takashi, 2021. "A simple algorithm for global sensitivity analysis with Shapley effects," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    4. Yao, Fang & Muller, Hans-Georg & Wang, Jane-Ling, 2005. "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 577-590, June.
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    1. Vuillod, Bruno & Montemurro, Marco & Panettieri, Enrico & Hallo, Ludovic, 2023. "A comparison between Sobol’s indices and Shapley’s effect for global sensitivity analysis of systems with independent input variables," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    2. Haibo Yang & Jiaqi Huang & Xinyi Liu & Peng Xu & Bo Yu & Jinjun Guo & Junhua Li & Yuhang Zhou & Xiaosong Shu, 2025. "Integration of Morris and GLUE methods for improving massflow-based debris flow simulation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(11), pages 12589-12612, June.
    3. Gökçe Koç & Ayşe Uzmay, 2024. "Construction of a Farm-Level Food Security Index: Case Study of Turkish Dairy Farms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 175(2), pages 687-714, November.
    4. Zhang, Xiaodong & Dimitrov, Nikolay, 2024. "Variable importance analysis of wind turbine extreme responses with Shapley value explanation," Renewable Energy, Elsevier, vol. 232(C).

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