Flood risk mapping and performance efficiency evaluation of machine learning algorithms: Best practice in northern Iran

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This analysis compares multiple machine learning methods for predicting flood risk, indicating the effectiveness of tree-based models.

XGBoost achieved the highest Area Under the Curve (AUC) of 0.87, demonstrating its superior predictive ability in flood risk mapping.
Multiple machine learning algorithms were applied in flood risk assessment, highlighting the importance of using advanced ML techniques.
Environmental parameters such as Digital Elevation Model and Topographic Wetness Index were crucial for accurate flood susceptibility modeling.
The findings emphasize the effectiveness of ensemble methods, especially in complex environmental contexts like northern Iran.

Shirmohammadi et al. (2025) studied this question.

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View Full Paper

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Authors

This analysis compares multiple machine learning methods for predicting flood risk, indicating the effectiveness of tree-based models.

XGBoost achieved the highest Area Under the Curve (AUC) of 0.87, demonstrating its superior predictive ability in flood risk mapping.
Multiple machine learning algorithms were applied in flood risk assessment, highlighting the importance of using advanced ML techniques.
Environmental parameters such as Digital Elevation Model and Topographic Wetness Index were crucial for accurate flood susceptibility modeling.
The findings emphasize the effectiveness of ensemble methods, especially in complex environmental contexts like northern Iran.

Shirmohammadi et al. (2025) studied this question.

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