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Gaussian process metamodeling of functional-input code for coastal flood hazard assessment

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  • Betancourt, José
  • Bachoc, François
  • Klein, Thierry
  • Idier, Déborah
  • Pedreros, Rodrigo
  • Rohmer, Jérémy

Abstract

This paper investigates the construction of a metamodel for coastal flooding early warning at the peninsula of Gâvres, France. The code under study is an hydrodynamic model which receives time-varying maritime conditions as inputs. We concentrate on Gaussian pocess metamodels to emulate the behavior of the code. To model the inputs we make a projection of them onto a space of lower dimension. This setting gives rise to a model selection methodology which we use to calibrate four characteristics of our functional-input metamodel: (i) the family of basis functions to project the inputs; (ii) the projection dimension; (iii) the distance to measure similarity between functional input points; and (iv) the set of functional predictors to keep active. The proposed methodology seeks to optimize these parameters for metamodel predictability, at an affordable computational cost. A comparison to a dimensionality reduction approach based on the projection error of the input functions only showed that the latter may lead to unnecessarily large projection dimensions. We also assessed the adaptability of our methodology to changes in the number of training and validation points. The methodology proved its robustness by finding the optimal solution for most of the instances, while being computationally efficient.

Suggested Citation

  • Betancourt, José & Bachoc, François & Klein, Thierry & Idier, Déborah & Pedreros, Rodrigo & Rohmer, Jérémy, 2020. "Gaussian process metamodeling of functional-input code for coastal flood hazard assessment," Reliability Engineering and System Safety, Elsevier, vol. 198(C).
  • Handle: RePEc:eee:reensy:v:198:y:2020:i:c:s0951832019301693
    DOI: 10.1016/j.ress.2020.106870
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    Cited by:

    1. Mert Edali, 2022. "Pattern‐oriented analysis of system dynamics models via random forests," System Dynamics Review, System Dynamics Society, vol. 38(2), pages 135-166, April.
    2. Marrel, Amandine & Iooss, Bertrand, 2024. "Probabilistic surrogate modeling by Gaussian process: A new estimation algorithm for more robust prediction," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    3. Wang, Yuhao & Gao, Yi & Liu, Yongming & Ghosh, Sayan & Subber, Waad & Pandita, Piyush & Wang, Liping, 2021. "Bayesian-entropy gaussian process for constrained metamodeling," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    4. Marrel, Amandine & Iooss, Bertrand, 2024. "Probabilistic surrogate modeling by Gaussian process: A review on recent insights in estimation and validation," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    5. Jung, WoongHee & Taflanidis, Alexandros A. & Kyprioti, Aikaterini P. & Zhang, Jize, 2024. "Adaptive multi-fidelity Monte Carlo for real-time probabilistic storm surge predictions," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    6. Fahad, Md Golam Rabbani & Nazari, Rouzbeh & Motamedi, M.H. & Karimi, Maryam, 2022. "A Decision-Making Framework Integrating Fluid and Solid Systems to Assess Resilience of Coastal Communities Experiencing Extreme Storm Events," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    7. López-Lopera, Andrés F. & Idier, Déborah & Rohmer, Jérémy & Bachoc, François, 2022. "Multioutput Gaussian processes with functional data: A study on coastal flood hazard assessment," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).

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