HaorFloodAlert combines random forest and XGBoost in a deseasonalized ensemble to achieve 89.6% LOOCV accuracy and 0.943 AUC-ROC for 72-hour flood prediction on 77 Sentinel-1 events in haor wetlands.
Rapid flood inundation mapping for effective man- agement: A machine learning and pixel-based classification approach in Feni District, Bangladesh
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HaorFloodAlert: Deseasonalized ML Ensemble for 72-Hour Flood Prediction in Bangladesh Haor Wetlands
HaorFloodAlert combines random forest and XGBoost in a deseasonalized ensemble to achieve 89.6% LOOCV accuracy and 0.943 AUC-ROC for 72-hour flood prediction on 77 Sentinel-1 events in haor wetlands.