Comparative assessment of machine learning models for flood susceptibility mapping

M. Heydari; A. Fazlollahi; S. P. Nainiva

doi:10.1007/s42108-026-00478-9

January 27, 2026International Journal of Energy and Water Resources

Comparative assessment of machine learning models for flood susceptibility mapping

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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.

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Implication

Comparative analysis assesses predictive accuracy of machine learning models for flood susceptibility mapping, indicating useful tools for disaster management.

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Heydari et al. (2026) studied this question.

synapsesocial.com/papers/69a75b21c6e9836116a21e11 https://doi.org/10.1007/s42108-026-00478-9

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Comparative assessment of machine learning models for flood susceptibility mapping

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Comparative assessment of machine learning models for flood susceptibility mapping

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