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.

翻译：孟加拉国豪拉（haor）湿地的山洪暴发几乎毫无预警，严重破坏每年boro水稻的收成。现有系统针对河流洪水设计，完全忽略了回水动力学机制。这些盆地地势平坦，水流行为与布拉马普特拉河截然不同。我们构建了HaorFloodAlert——一个去季节化的机器学习集成模型，可预测苏纳姆甘杰豪拉地区（约8000平方公里）未来72小时的洪水概率。温度原本充当着季节性作弊器——仅因洪水发生在温暖月份，就虚增了6.9个百分点的预测精度，我们识别了这一偏差。此外，基于印度锡尔杰尔上游巴拉克河的Sentinel-1 SAR代理数据，可提供约36小时的预警提前量。经大津阈值处理的SAR变化检测显示空间匹配度达84%-91%。运行态集成模型（随机森林权重0.5625 + XGBoost权重0.4375）在77个真实Sentinel-1事件中实现89.6%的留一交叉验证准确率、87.5%的召回率及0.943的AUC-ROC值。系统还包含三级预警流水线与经BRRI校准的boro水稻损失评估模块。