Articles | Volume 28, issue 4
https://doi.org/10.5194/hess-28-945-2024
https://doi.org/10.5194/hess-28-945-2024
Research article
 | 
27 Feb 2024
Research article |  | 27 Feb 2024

Toward interpretable LSTM-based modeling of hydrological systems

Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon

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Cited articles

Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. 
Addor, N., Nearing, G., Prieto, C., Newman, A. J., Le Vine, N., and Clark, M. P.: A Ranking of Hydrological Signatures Based on Their Predictability in Space, Water Resour. Res., 54, 8792–8812, https://doi.org/10.1029/2018WR022606, 2018. 
Ali, G., Tetzlaff, D., Soulsby, C., McDonnell, J. J., and Capell, R.: A comparison of similarity indices for catchment classification using a cross-regional dataset, Adv. Water Resour., 40, 11–22, https://doi.org/10.1016/j.advwatres.2012.01.008, 2012. 
Breiman, L.: Random Forest, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. 
Burnash, R., Ferral, L., and McGuire, R.: A Generalized Streamflow Simulation System: Conceptual Modeling for Digital Computers, U.S. Department of Commerce, National Weather Service, and State of California, Department of Water Resources, 204 pp., https://www.google.com/books/edition/A_Generalized_Streamflow_Simulation_Syst/aQJDAAAAIAAJ?hl=en (last access: January 2023), 1973. 
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Short summary
Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
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