Robust hourly flood forecasting using LSTM: Handling missing inputs and integrating discharge observations

Long Short-Term Memory (LSTM) networks have demonstrated state-of-the-art performance for operational flood forecasting at the daily scale (Nearing et al., 2024). Recent advances have extended LSTM-based models to higher temporal resolutions through multi-frequency LSTM architectures (Acuña Espinoza et al., 2025) and introduced robust strategies for handling missing data, such as masked-mean embeddings (Gauch et al., 2025).

Building on this work, we introduce an LSTM-based approach that allows producing hourly flood forecasts in an operational setting, while being robust to missing data. Moreover, using masked-mean embeddings plus teacher-forcing (Williams et al., 1989) and noise injection strategies during training, allows the model to integrate observed stream flow observations when available, for enhanced prediction accuracy, while keeping the flexibility to operate without this signal. 

To evaluate model performance, we benchmarked the new approach against LARSIM, the current operational model in many federal states in Germany. Our results show that the LSTM-based architecture outperforms the LARSIM model in predictive accuracy, while additionally offering robustness to missing inputs and faster inference times.

These findings highlight the potential of deep learning–based models for operational flood forecasting at an hourly resolution, while introducing strategies to increase robustness and add valuable information, when available. 

 

Reference:

Acuña Espinoza, E., Kratzert, F., Klotz, D., Gauch, M., Álvarez Chaves, M., Loritz, R., & Ehret, U. (2025). Technical note: An approach for handling multiple temporal frequencies with different input dimensions using a single LSTM cell. Hydrology and Earth System Sciences, 29(6), 1749–1758. https://doi.org/10.5194/hess-29-1749-2025

Gauch, M., Kratzert, F., Klotz, D., Nearing, G., Cohen, D., & Gilon, O. (2025). How to deal with missing input data. Hydrology and Earth System Sciences, 29(21), 6221–6235. https://doi.org/10.5194/hess-29-6221-2025

Nearing, G., Cohen, D., Dube, V., Gauch, M., Gilon, O., Harrigan, S., Hassidim, A., Klotz, D., Kratzert, F., Metzger, A., Nevo, S., Pappenberger, F., Prudhomme, C., Shalev, G., Shenzis, S., Tekalign, T. Y., Weitzner, D., & Matias, Y. (2024). Global prediction of extreme floods in ungauged watersheds. Nature, 627(8004), 559–563. https://doi.org/10.1038/s41586-024-07145-1

Williams, R. J., & Zipser, D. (1989). A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1(2), 270-280.