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Reliable prediction of river floods in the first 72 hours can reduce harmbecause emergency agencies have sufficient time to prepare and deploy for helpat the scene. Such river flood prediction models already exist and performrelatively well in most high-income countries. But, due to the limitedavailability of data, these models are lacking in low-income countries. Here,we offer the first global river flood prediction framework based on the newlypublished Caravan dataset. Our framework aims to serve as a benchmark forfuture global river flood prediction research. To support generalizabilityclaims we include custom data evaluation splits. Further, we propose andevaluate a novel two-path LSTM architecture (2P-LSTM) against three baselinemodels. Finally, we evaluate the generated models on different locations inAfrica and Asia that were not part of the Caravan dataset.

ML framework for global river flood predictions based on the Caravan dataset