August 24, 2025HydrologyOpen Access

Generalized Methodology for Two-Dimensional Flood Depth Prediction Using ML-Based Models

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This analysis demonstrates improved flood depth prediction in various catchments, highlighting the utility of machine learning models.

The study achieves a strong R2 of 83% for testing using a catchment-based clustering regression model, emphasizing predictive capability.
Machine learning models significantly outperform traditional methods, showing a dramatic speed increase for flood depth predictions.
Assessment included multiple models such as Random Forest, achieving up to 75% testing R2 when applying pixel clustering regression.
Adopting this methodology may enable effective flood early warning systems utilizing freely available datasets.

Soliman et al. (2025) studied this question.

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