Machine learning system provides 72-hour flash flood warnings for Bangladesh wetlands

Researchers have developed HaorFloodAlert, a machine learning system designed to provide 72-hour early warnings for flash floods in Bangladesh's Haor wetlands. The system utilizes free data sources including Sentinel-1 radar, rainfall records and forecasts, soil moisture, and upstream river data. By removing seasonal bias from temperature data, the model achieves high accuracy, with an ensemble of Random Forest and XGBoost reaching 90.9% accuracy and an AUC of 0.939 in testing. Warnings are disseminated to farmers via SMS, email, and WhatsApp in Bengali. AI

IMPACT This system demonstrates the potential for ML to provide critical, life-saving early warnings in vulnerable regions.

RANK_REASON The cluster describes a research paper detailing a new machine learning system for flood prediction. [lever_c_demoted from research: ic=1 ai=1.0]

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• Bangladesh
• Barak River
• HaorFloodAlert
• Haor Wetlands
• India
• random forest
• Salma Hoque Talukdar Koli
• Sentinel-1
• Sunamganj Haor
• XGBoost

• paper
• product
• other

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