Comparative assessment of machine learning models for flood susceptibility mapping

Key Points

• Mapping flood susceptibility using various machine learning models yields significant insights, enhancing predictive accuracy.
• Top-performing models achieve over 85% accuracy, demonstrating their utility in assessing flood risks effectively.
• Comparative assessment across multiple algorithms highlights distinct strengths, enabling improved decision-making in flood management.
• Findings support the integration of machine learning in disaster preparedness frameworks for mitigating flood impacts.