Linking Landscapes, Flood Risks and Communities: Flood Susceptibility Assessment of Connemara and Aaran Islands, Galway, Ireland

Atlantic‐facing coastal communities of western Ireland face recurrent flood hazards driven by intense precipitation, storm surges, and tidal influences. Existing national flood mapping efforts provide valuable hydrodynamic simulations but rarely integrate multi‐criteria decision analysis with data‐driven machine learning to capture local‐scale flood susceptibilities. This study provides a first integrated Analytical Hierarchy Process (AHP) and Machine Learning (ML) for Atlantic‐facing of Connemara and Aaran Islands, County Galway. The integrated AHP‐ML approach outperforms standalone ML models by integrating transparent, expert‐driven weights that reflect local geomorphology and data constraints often missed by non‐linear ML algorithms. Nine flood influencing parameters: rainfall, elevation, slope, curvature, land cover, distance to rivers, soil type, and normalised differential vegetation indexand four ML models (Logistic Regression, Random Forest, XGBoost, and Support Vector Machines) are considered for the study. The model performances were qualified using the area under the receiver operating characteristic (AUC), precision and recall. The probabilistic benchmarking validation was also performed against the GloFAS 100‐year flood hazard product, DEM‐based Sea Level Rise (+1 m, +2 m, +5 m and +10 m) scenarios, and OPW flood records. The AHP results indicate that 1.36% and 33.13% of the study area falls within ‘very high’ (16.92 km 2 ) and ‘high’ (410 Km 2 ) flood susceptibility zones. A spatially separated evaluation of the ML models, showed highest predictive accuracy for XGBoost (73%) closely followed by SVM (68%), Random Forest (64%) and Logistic Regression (63%). Discrimination and calibration were moderate (ROC‐AUC≈0.70, PR‐AUC≈0.70, PR‐AUC≈0.75, Brier ≈0.206) for the best‐performing XGBoost Model. The socio‐demographic data overlaid on the final susceptibility classes reveals that a total population of 11,318 people (with approximately 24% elderly and 19% children age groups) were vulnerable to high flood susceptibility. The study findings demonstrate the need for integrating complementary modelling approaches to enhance flood susceptibility assessments in mid‐latitude Irish coastal and remote regions.