Applying machine learning models for flood susceptibility mapping in Thessaly, Greece

Machine learning techniques have been increasingly used in flood management worldwide to enhance the effectiveness of traditional methods for flood susceptibility mapping. Although these models have achieved higher accuracy than traditional ones, their application in Greece remains limited. We focus on applying machine learning models to create flood susceptibility maps for Thessaly, Greece, a flood-prone region with extreme flood events recorded in recent years. The study integrates topographical, hydrological, hydraulic, environmental and infrastructure data to train the models. The results demonstrate the potential of machine learning in providing accurate and practical flood risk information to enhance flood management and support decision-making for disaster preparedness in Thessaly.