ML framework for global river flood predictions based on the Caravan dataset
This work addresses the lack of flood prediction models for low-income countries, providing a benchmark for global research, though it is incremental as it builds on existing methods with new data.
The paper tackles the problem of unreliable river flood predictions in low-income countries by developing a global framework using the Caravan dataset, achieving benchmark results with a novel two-path LSTM architecture that outperforms baselines in evaluations on locations in Africa and Asia not included in the dataset.
Reliable prediction of river floods in the first 72 hours can reduce harm because emergency agencies have sufficient time to prepare and deploy for help at the scene. Such river flood prediction models already exist and perform relatively well in most high-income countries. But, due to the limited availability of data, these models are lacking in low-income countries. Here, we offer the first global river flood prediction framework based on the newly published Caravan dataset. Our framework aims to serve as a benchmark for future global river flood prediction research. To support generalizability claims we include custom data evaluation splits. Further, we propose and evaluate a novel two-path LSTM architecture (2P-LSTM) against three baseline models. Finally, we evaluate the generated models on different locations in Africa and Asia that were not part of the Caravan dataset.