August 17, 2025Zorig Melong.Open Access

Enhancing Flood Susceptibility Mapping Through Machine Learning and Multi-Criteria Decision-Making Techniques

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Research demonstrates high flood map accuracy through machine learning in Toorsa and Pasakha Rivers, indicating effective risk management.

High accuracy in flood susceptibility mapping reached with Random Forest model, showing Area Under Curve score of 0.98.
Machine learning outperformed multi-criteria decision models in classifying flood-prone areas, enhancing mapping precision.
Shannon Entropy and Criteria Importance methods analyzed interrelationships among factors like elevation and rainfall.
Findings support using machine learning for reliable flood risk assessments, crucial for land use planning and preparedness.

Pasang et al. (2025) studied this question.

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Research demonstrates high flood map accuracy through machine learning in Toorsa and Pasakha Rivers, indicating effective risk management.