HaorFloodAlert: Deseasonalized ML Ensemble for 72-Hour Flood Prediction in Bangladesh Haor Wetlands
Researchers deployed a deseasonalized ML ensemble to forecast flash floods in Bangladesh's haor wetlands, a domain where standard riverine models fail due to flat basin hydrology. The system catches a critical methodological trap: temperature inflates accuracy by nearly 7 percentage points simply because floods cluster in warm months, not because temperature predicts flood mechanics. By removing this seasonal confound and layering Sentinel-1 SAR change detection from upstream Assam as a 36-hour proxy signal, the ensemble achieves 84-91 percent spatial validation. This work exemplifies how domain-specific ML requires adversarial scrutiny of feature leakage and multimodal sensor fusion to move from lab benchmarks to operational utility in climate-vulnerable regions.58
