Articles | Volume 26, issue 3
https://doi.org/10.5194/hess-26-795-2022
https://doi.org/10.5194/hess-26-795-2022
Research article
 | 
14 Feb 2022
Research article |  | 14 Feb 2022

Evaluation and interpretation of convolutional long short-term memory networks for regional hydrological modelling

Sam Anderson and Valentina Radić

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Cited articles

Anderson, S.: andersonsam/cnn_lstm_era: First release (Version v1.0.0), Zenodo [code], https://doi.org/10.5281/ZENODO.5181175, 2021. 
Anderson, S. and Radić, V.: Identification of local water resource vulnerability to rapid deglaciation in Alberta, Nat. Clim. Change, 10, 933–938, https://doi.org/10.1038/s41558-020-0863-4, 2020. 
Arras, L., Arjona-Medina, J., Widrich, M., Montavon, G., Gillhofer, M., Müller, K.-R., Hochreiter, S., and Samek, W.: Explaining and Interpreting LSTMs BT – Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, edited by: Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., and Müller, K.-R., Springer International Publishing, Cham, 211–238, https://doi.org/10.1007/978-3-030-28954-6_11, 2019. 
Assem, H., Ghariba, S., Makrai, G., Johnston, P., Gill, L., and Pilla, F.: Urban Water Flow and Water Level Prediction Based on Deep Learning, in: ECML PKDD 2017: Machine Learning and Knowledge Discovery in Databases, Springer, Cham, 317–329, https://doi.org/10.1007/978-3-319-71273-4_26, 2017. 
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Short summary
We develop and interpret a spatiotemporal deep learning model for regional streamflow prediction at more than 200 stream gauge stations in western Canada. We find the novel modelling style to work very well for daily streamflow prediction. Importantly, we interpret model learning to show that it has learned to focus on physically interpretable and physically relevant information, which is a highly desirable quality of machine-learning-based hydrological models.