Multi-Output Gaussian Processes with Functional Data: A Study on Coastal Flood Hazard Assessment
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Most of the existing coastal flood Forecast and Early-Warning Systems do not model the flood, but instead, rely on the prediction of hydrodynamic conditions at the coast and on expert judgment. Recent scientific contributions are now capable to precisely model flood events, even in situations where wave overtopping plays a significant role. Such models are nevertheless costly-to-evaluate and surrogate ones need to be exploited for substantial computational savings. For the latter models, the hydro-meteorological forcing conditions (inputs) or flood events (outputs) are conveniently parametrised into scalar representations. However, they neglect the fact that inputs are actually functions (more precisely, time series), and that floods spatially propagate inland. Here, we introduce a multi-output Gaussian process model accounting for both criteria. On various examples, we test its versatility for both learning spatial maps and inferring unobserved ones. We demonstrate that efficient implementations are obtained by considering tensor-structured data and/or sparse-variational approximations. Finally, the proposed framework is applied on a coastal application aiming at predicting flood events. We conclude that accurate predictions are obtained in the order of minutes rather than the couples of days required by dedicated hydrodynamic simulators.
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