IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v222y2022ics0951832022000904.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832022000904
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2022.108420?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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. Zhang, Xiaodong & Dimitrov, Nikolay, 2024. "Variable importance analysis of wind turbine extreme responses with Shapley value explanation," Renewable Energy, Elsevier, vol. 232(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiong, Qingwen & Du, Peng & Deng, Jian & Huang, Daishun & Song, Gongle & Qian, Libo & Wu, Zenghui & Luo, Yuejian, 2022. "Global sensitivity analysis for nuclear reactor LBLOCA with time-dependent outputs," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    2. Barr, John & Rabitz, Herschel, 2023. "Kernel-based global sensitivity analysis obtained from a single data set," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Wang, Jingxing & Chung, Seokhyun & AlShelahi, Abdullah & Kontar, Raed & Byon, Eunshin & Saigal, Romesh, 2021. "Look-ahead decision making for renewable energy: A dynamic “predict and store” approach," Applied Energy, Elsevier, vol. 296(C).
    4. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    5. Guangxing Wang & Sisheng Liu & Fang Han & Chong‐Zhi Di, 2023. "Robust functional principal component analysis via a functional pairwise spatial sign operator," Biometrics, The International Biometric Society, vol. 79(2), pages 1239-1253, June.
    6. Li, Pai-Ling & Chiou, Jeng-Min & Shyr, Yu, 2017. "Functional data classification using covariate-adjusted subspace projection," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 21-34.
    7. Poskitt, D.S. & Sengarapillai, Arivalzahan, 2013. "Description length and dimensionality reduction in functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 98-113.
    8. Lu, Xuefei & Borgonovo, Emanuele, 2023. "Global sensitivity analysis in epidemiological modeling," European Journal of Operational Research, Elsevier, vol. 304(1), pages 9-24.
    9. González-Rodríguez, Gil & Colubi, Ana, 2017. "On the consistency of bootstrap methods in separable Hilbert spaces," Econometrics and Statistics, Elsevier, vol. 1(C), pages 118-127.
    10. Shuxi Zeng & Elizabeth C. Lange & Elizabeth A. Archie & Fernando A. Campos & Susan C. Alberts & Fan Li, 2023. "A Causal Mediation Model for Longitudinal Mediators and Survival Outcomes with an Application to Animal Behavior," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 197-218, June.
    11. J. Q. Shi & B. Wang & R. Murray-Smith & D. M. Titterington, 2007. "Gaussian Process Functional Regression Modeling for Batch Data," Biometrics, The International Biometric Society, vol. 63(3), pages 714-723, September.
    12. Weishampel, Anthony & Staicu, Ana-Maria & Rand, William, 2023. "Classification of social media users with generalized functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    13. Nagy, Stanislav & Ferraty, Frédéric, 2019. "Data depth for measurable noisy random functions," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 95-114.
    14. Chenlin Zhang & Huazhen Lin & Li Liu & Jin Liu & Yi Li, 2023. "Functional data analysis with covariate‐dependent mean and covariance structures," Biometrics, The International Biometric Society, vol. 79(3), pages 2232-2245, September.
    15. Tomáš Rubín & Victor M. Panaretos, 2020. "Functional lagged regression with sparse noisy observations," Journal of Time Series Analysis, Wiley Blackwell, vol. 41(6), pages 858-882, November.
    16. Beran, Jan & Liu, Haiyan, 2016. "Estimation of eigenvalues, eigenvectors and scores in FDA models with dependent errors," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 218-233.
    17. Golovkine, Steven & Klutchnikoff, Nicolas & Patilea, Valentin, 2022. "Clustering multivariate functional data using unsupervised binary trees," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    18. Pengjie Zhou & Haoyu Wei & Huiming Zhang, 2024. "Selective Reviews of Bandit Problems in AI via a Statistical View," Papers 2412.02251, arXiv.org.
    19. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    20. Tomasz Górecki & Lajos Horváth & Piotr Kokoszka, 2020. "Tests of Normality of Functional Data," International Statistical Review, International Statistical Institute, vol. 88(3), pages 677-697, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:222:y:2022:i:c:s0951832022000904. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.