An XBeach-informed machine-learning surrogate for rapid coastal flood prediction

Abstract. Rapid assessment of coastal inundation along the Western Black Sea coast requires computationally efficient methods that can screen multiple compound storm scenarios while preserving a clear link to the controlling physical drivers. This study develops and evaluates an XBeach-informed machine-learning surrogate for coastal inundation and threshold-based flood-extent screening. The framework combines high-frequency sea-level observations from the Maritime Hydrographic Directorate monitoring network, Copernicus Marine wave and Black Sea physical products, ERA5 atmospheric forcing, Global Runoff Data Centre Danube discharge, bathymetric and coastal-elevation descriptors, and an intermediate cross-shore physical-response layer. The modelling dataset consists of 5,000 Monte Carlo forcing scenarios, 22 forcing and derived predictors, and a 10 × 10 spatial target grids, corresponding to 100 inundation-depth nodes per scenario. Four tree-based residual models, namely Random Forest, Gradient Boosting, Extra Trees, and Histogram Gradient Boosting, are combined using validation-derived weights.

The conservative held-out node-level evaluation shows moderate quantitative skill for continuous inundation-depth prediction. The ensemble reaches R² = 0.409, RMSE = 1.181 m, MAE = 0.678 m, NSE = 0.409, KGE = 0.235, Pearson r = 0.914, and Spearman ρ = 0.923. In contrast, threshold-based flood-extent classification above the 0.30 m operational inundation threshold is substantially stronger, with F1 = 0.995, AUC-ROC = 0.9998, Matthews correlation coefficient = 0.989, and Cohen’s κ = 0.989. Split conformal prediction intervals provide near-nominal 90 % marginal coverage, with empirical coverage of 0.911, but the mean 90 % interval width is large, 3.25 m, indicating limited sharpness for local quantitative depth estimation. Extreme-event diagnostics show systematic underprediction of upper-tail inundation depths, with a mean bias of approximately −2.59 m for events above the 90th percentile.

The present configuration is therefore most appropriate for rapid flood-extent screening, scenario ranking, and identification of cases where more detailed process-based simulations are required. It should not yet be interpreted as a stand-alone operational predictor of maximum inundation depth. The study demonstrates how in situ observations, Copernicus Marine products, physical-response modelling, machine-learning emulation, and uncertainty diagnostics can be combined into a transparent coastal-hazard screening workflow for the Western Black Sea coast.