Tracking the critical offshore conditions leading to inundation via active learning of full-process based models

Abstract

High-fidelity numerical models (full process based) facilitate accurate simulations of storm responses. For efficient implementation in probabilistic risk assessment, the simulation of a large number (<10,000s) of combinations of offshore hydrodynamic conditions (e.g. wave characteristics, offshore water level, etc.) is often necessary. To optimise this procedure, it can be of interest to concentrate the computation effort by only identifying the critical set of offshore conditions that lead to inundation on key assets for the studied territory (e.g., evacuation routes, hospitals, etc.). However, two limitations exist: 1. full-process based models have large computation time cost, typically of several hours, which often prevent from conducting several simulation scenarios; 2. the full-process based models are expected to present non-linearities (non-regularities) or shocks (discontinuities). In this study, we propose a strategy combining meta-modelling (of type Support Vector Machine) and active learning techniques to track with a limited number of long-running simulations the critical set’s boundary. The developments are done on a cross-shore case, using the process-based SWASH model (computational time of 10 hours for one run). The dynamic forcing conditions are parametrized by storm surge S and significant wave height Hs. We validated the approach with respect to a reference set of 400 long-running simulations in the domain of (S ; Hs). Our tests showed that the tracking of the critical contour can be achieved with a reasonable number of long-running simulations of a few tens.