Articles | Volume 26, issue 11
https://doi.org/10.5194/nhess-26-5667-2026
https://doi.org/10.5194/nhess-26-5667-2026
Research article
 | 
05 Nov 2026
Research article |  | 05 Nov 2026

From forecast to alert: an open-data AI flood early warning system with reservoir monitoring for the White Volta Basin

Joseph Junior Obeng, Hongyong Yuan, Tao Chen, and Lida Huang

Cited articles

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Cao, X., Yao, Y., Wang, Z., Zhao, Z., Borthwick, A. G. L., and Qin, H.: Large-scale urban flood modeling and zero-shot high-resolution generalization with LarNO, J. Hydrol., 676, 135686, https://doi.org/10.1016/j.jhydrol.2026.135686, 2026. 
Chini, M., Pelich, R., Pulvirenti, L., Pierdicca, N., Hostache, R., and Matgen, P.: Sentinel-1 InSAR coherence to detect floodwater in urban areas: Houston and Hurricane Harvey as a test case, Remote Sens., 11, 107, https://doi.org/10.3390/rs11020107, 2019. 
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Short summary
Flooding in Northern Ghana's White Volta Basin harms communities, often from unplanned water releases from a neighboring country's dam. Existing global warning systems overlook this cross-border risk, so we created an easy-to-use flood early warning system using Open data, artificial intelligence and reservoir monitoring. It forecasts river flows 1–5 days ahead with high accuracy, outperforming current tools.
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