用卫星数据和机器学习，提前72小时预警孟加拉国洪涝区的突发洪水。
HaorFloodAlert: A 72-Hour Machine Learning Early Warning System for Flash Floods in Bangladesh's Haor Wetlands
- 融合卫星雷达、降雨和上游河流信号，构建无监督预警模型。
- 在77次真实洪灾事件中准确率达90.9%，F1-score达89.2%。
- 专为农民设计，通过短信、邮件等多渠道发送本地化预警。
每年春季，孟加拉国东北部的哈奥湿地在二月稻收获前遭遇突发性洪水，一次洪灾可在数日内摧毁一个家庭全部作物。由于孙纳姆甘吉哈奥是平坦碗状盆地，仅靠本地降雨、国内河流及印度巴拉克河同时注水，而覆盖8000平方公里的监测站不足12个，导致预警困难。现有模型因使用随季节变化的温度数据，实际学习的是日历周期而非洪水规律，且无法向农户发出警告。本文提出的HaorFloodAlert系统仅依赖免费数据：可穿透云层的哨兵-1雷达影像、降雨观测与预报、土壤湿度及模拟的上游巴拉克河信号（约提供36小时提前量）。通过月度气候异常剔除季节偏移，将温度标签相关性从r=0.570降至r=-0.031。在2014–2024年77次真实事件的留一交叉验证中，随机森林与XGBoost集成模型达到90.9%准确率、89.2% F1-score、AUC 0.939，且结果与12.3年官方水位计记录一致。该系统于2026年5–6月实时运行10天，提前三天发出高风险警报。预警以孟加拉语通过短信、邮件、WhatsApp推送，所有数据均可由公开种子管道复现。
原文摘要 · Abstract (English)
Every spring, flash floods strike the haor wetlands of northeast Bangladesh just before the boro rice harvest, and one flood can erase a family's entire crop in days. Warning people in time is hard here for a structural reason: the Sunamganj Haor is a flat, bowl-shaped basin that fills at once from local rain, domestic rivers, and the Barak River in India, while fewer than twelve working gauges cover its 8,000 km2. Existing models add a quieter problem of their own, because they train on raw temperature, which simply follows the season, so they learn the calendar instead of the flood, and none of them delivers a warning to a farmer. HaorFloodAlert answers both problems with free data alone: Sentinel-1 radar that sees through storm clouds, rainfall records and forecasts, soil moisture, and a modeled upstream Barak signal worth about 36 hours of lead time. A monthly climatological anomaly then removes the seasonal bias, cutting the temperature-label correlation from r=0.570 to r=-0.031. Tested by leave-one-out cross-validation on 77 events with real Sentinel-1 images (2014-2024), the Random Forest and XGBoost ensemble reaches 90.9% accuracy, 89.2% F1-score, and AUC 0.939, and these labels hold up against 12.3 years of official gauge records. The same system then ran live for ten days in May-June 2026 and raised a high-risk alert about three days before the river neared its danger level. Warnings go out in Bengali by SMS, e-mail, and WhatsApp, and every number here can be regenerated from our public, seeded pipeline.
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