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Article Dans Une Revue Reliability Engineering and System Safety Année : 2021

Multioutput Gaussian processes with functional data: A study on coastal flood hazard assessment

Résumé

Surrogate models are often used to replace costly-to-evaluate complex coastal codes to achieve substantial computational savings. In many of those models, the hydrometeorological forcing conditions (inputs) or flood events (outputs) are conveniently parameterized by scalar representations, neglecting that the inputs are actually time series and that floods propagate spatially inland. Both facts are crucial in flood prediction for complex coastal systems. Our aim is to establish a surrogate model that accounts for time-varying inputs and provides information on spatially varying inland flooding. We introduce a multioutput Gaussian process model based on a separable kernel that correlates both functional inputs and spatial locations. Efficient implementations consider tensor-structured computations or sparse-variational approximations. In several experiments, we demonstrate the versatility of the model for both learning maps and inferring unobserved maps, numerically showing the convergence of predictions as the number of learning maps increases. We assess our framework in a coastal flood prediction application. Predictions are obtained with small error values within computation time highly compatible with short-term forecast requirements (on the order of minutes compared to the days required by hydrodynamic simulators). We conclude that our framework is a promising approach for forecast and early-warning systems.
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Dates et versions

hal-03412417 , version 1 (03-11-2021)
hal-03412417 , version 2 (08-12-2023)

Identifiants

Citer

Andrés F. López-Lopera, Déborah Idier, Jeremy Rohmer, François Bachoc. Multioutput Gaussian processes with functional data: A study on coastal flood hazard assessment. Reliability Engineering and System Safety, 2021, 218, pp.108139. ⟨10.1016/j.ress.2021.108139⟩. ⟨hal-03412417v2⟩
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