Articles | Volume 29, issue 19
https://doi.org/10.5194/hess-29-5131-2025
https://doi.org/10.5194/hess-29-5131-2025
Research article
 | 
14 Oct 2025
Research article |  | 14 Oct 2025

Neural networks in catchment hydrology: a comparative study of different algorithms in an ensemble of ungauged basins in Germany

Max Weißenborn, Lutz Breuer, and Tobias Houska

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Cited articles

Afzaal, H., Farooque, A. A., Abbas, F., Acharya, B., and Esau, T.: Groundwater Estimation from Major Physical Hydrology Components Using Artificial Neural Networks and Deep Learning, Water, 12, 5, https://doi.org/10.3390/w12010005, 2019. a, b
Bai, S., Kolter, J. Z., and Koltun, V.: An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling, arXiv [preprint], arXiv:1803.01271, https://doi.org/10.48550/arXiv.1803.01271, 2018. a
Bhattacharjee, R., Ghosh, D., and Mazumder, A.: A review on hyper-parameter optimisation by deep learning experiments, Journal of Mathematical Sciences & Computational Mathematics, 2, 532–541, https://doi.org/10.15864/jmscm.2407, 2021. a
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Brunner, M. I., Slater, L., Tallaksen, L. M., and Clark, M.: Challenges in modeling and predicting floods and droughts: A review, Wires Water, 8, e1520, https://doi.org/10.1002/wat2.1520, 2021. a
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Short summary
Our study compares neural network models for predicting discharge in ungauged basins. We evaluated convolutional neural networks (CNNs), long short-term memory (LSTM) and gated recurrent units (GRUs) using 28 years of weather data. CNNs showed the best accuracy, while GRUs were faster and nearly as accurate. Adding static features improved all models. The research enhances flood forecasting and water management in regions lacking direct measurements, offering efficient and accurate predictive tools.
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