Fuzzy-decision trees models for flood hazard modeling in the Danube Delta

This study proposes an innovative methodological framework for assessing flood susceptibility in deltaic areas, by integrating fuzzy logic with three decision tree algorithms: CART, Random Forest and Alternating Decision Tree. The novelty of the research lies in the explicit combination of the ability of fuzzy logic to manage the uncertainty and ambiguity of geographical factors with the predictive power and interpretability of decision tree algorithms, thus responding to a gap in the specialized literature, where these methods are often applied separately or without a clear comparative justification. The methodology is justified by the specific complexity of deltaic areas, characterized by low altitudes, multiple hydrological interactions and variable anthropogenic influences, which classical or ‘black-box’ models incompletely capture. The study uses eight determinants, weighted by fuzzy coefficients validated by historical data, and evaluates the performance of the models using ROC, AUC and statistical indicators. The results show excellent performance for all models (AUC > 0.90), highlighting relevant functional differences: Fuzzy-CART offers a conservative approach, Fuzzy-RF ensures the highest accuracy and stability, and Fuzzy-ADT provides a balanced and nuanced assessment. The study provides a robust and transparent tool for flood risk management in the Danube Delta.