🤖 AI Summary
This study addresses the critical gap in flash flood early warning for the Haor wetlands of Bangladesh and the inability of existing models to adequately represent backwater dynamics. To overcome these challenges, the authors propose a deseasonalized machine learning ensemble approach that leverages Otsu-threshold-based change detection from Sentinel-1 SAR imagery to construct a proxy indicator for upstream Barak River conditions. A weighted ensemble of Random Forest and XGBoost models enables 72-hour probabilistic flood forecasting, integrated with a three-tier alert system and a BRRI-calibrated rice loss assessment module. By explicitly removing temperature-driven seasonal biases that artificially inflate accuracy, the framework delivers actionable lead times of approximately 36 hours. Evaluated against 77 historical flood events, the model achieves 89.6% accuracy, 87.5% recall, and an AUC-ROC of 0.943, with spatial validation showing 84–91% agreement.
📝 Abstract
Flash floods in Bangladesh's haor wetlands show up with almost no warning. They wreck the annual boro rice harvest. Current setups, built for riverine floods, miss backwater dynamics entirely. These basins are flat. Water does not behave like it does on the Brahmaputra.
We built HaorFloodAlert, a deseasonalized machine learning ensemble that forecasts 72-hour flood probability for the Sunamganj Haor (approximately 8,000 km2). Temperature was acting as a seasonal cheat code - it inflated accuracy by 6.9 pp just because floods happen in warm months. We caught that. We also built an upstream Barak River Sentinel-1 SAR proxy from Silchar, Assam, giving about 36 hours of lead time. Otsu-thresholded SAR change detection validates at 84-91 percent spatial match.
The operational ensemble (RF 0.5625 + XGBoost 0.4375) hits 89.6 percent LOOCV accuracy, 87.5 percent recall, and 0.943 AUC-ROC on 77 real Sentinel-1 events. A three-tier alert pipeline and a BRRI-calibrated boro rice damage estimator are included.