Explainable, Uncertainty-Aware Flood Susceptibility and Impact Mapping on Euboea Island (Greece) Using Google Earth Engine and Tree-Based Ensemble Models

Flooding remains among the most damaging hydro-meteorological hazards in the Mediterranean. On Euboea Island (Greece), steep terrain, ongoing land-use change, and highly connected transport corridors can intensify both flood occurrence and potential consequences. This study presents an integrated, decision-oriented framework that jointly maps (a) flood susceptibility and (b) flood impact potential (exposure), combining Google Earth Engine (GEE) for predictor generation with Python-based machine learning, explainability, and uncertainty analytics.

A multi-source predictor database is assembled in GEE from satellite and ancillary datasets to represent key topographic, climatic, geological, and pedological controls on flooding. Terrain and morphometric predictors are derived from the ALOS 12.5 m DEM, including elevation, slope angle, plan and profile curvature, Topographic Wetness Index (TWI), and Topographic Position Index (TPI). Hydrologic connectivity is captured through distance to the river/stream network. Climatic forcing is represented using the Modified Fournier Index (MFI) from WorldClim v2.0 as a predictor variable for rainfall influence. Subsurface controls are incorporated via lithology (geological map) and topsoil texture (LUCAS database; sand, silt, and clay content), which modulate infiltration, storage, and runoff generation. Land-surface conditions affecting runoff are characterized using CORINE Land Cover 2018, reflecting vegetation cover and imperviousness patterns. In parallel, exposure is quantified using land-use intensity, building footprint/coverage metrics, and road-network descriptors (density, proximity, connectivity) to identify areas where flood impacts are likely to be most severe.

Flood occurrence labels are derived from an event inventory, and spatially explicit sampling and partitioning are applied to reduce spatial autocorrelation and improve generalization. Susceptibility is modeled using tree-based ensembles (Random Forest and XGBoost), trained and evaluated in Python with spatial cross-validation and metrics capturing both discrimination and reliability (AUC, F1/TSS, Brier score, and calibration diagnostics). To explicitly communicate confidence and reveal spatial weaknesses, we generate uncertainty and entropy maps: (a) predictive uncertainty estimated from ensemble dispersion and calibrated probabilities, and (b) Shannon entropy of class probabilities to highlight ambiguous transition zones, data-sparse areas, and geomorphologically heterogeneous corridors. Explainability is delivered via SHAP (global and local), supported by interaction and partial dependence analyses to identify dominant controls and to attribute exposure hotspots to drivers such as building and road concentration.

The resulting susceptibility, exposure/impact, and uncertainty–entropy maps provide transparent, decision-relevant information to support mitigation prioritization and strengthen trustworthy flood-risk screening on Euboea Island.

Keywords: flood susceptibility; exposure; impact mapping; Google Earth Engine; Python; tree-based ensembles; uncertainty; predictive entropy; SHAP; explainable AI; Euboea; Greece