20 Apr 2023
 | 20 Apr 2023
Status: this preprint is open for discussion.

Towards Interpretable LSTM-based Modelling of Hydrological Systems

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

Abstract. Several studies have demonstrated the ability of Long Short-Term Memory (LSTM) machine learning based modeling to outperform traditional spatially lumped process-based modeling approaches for streamflow prediction. However, due mainly to the structural complexity of the LSTM network (which includes gating operations and sequential processing of the data), difficulties can arise when interpreting the internal processes and weights in the model.

Here, we propose and test a modification of LSTM architecture that represents internal system processes in a manner that is analogous to a hydrological reservoir. Our architecture, called HydroLSTM, simulates behaviors inherent in a dynamic system, such as sequential updating of the Markovian storage. Specifically, we modify how data is fed to the new representation to facilitate simultaneous access to past lagged inputs, thereby explicitly acknowledging the importance of trends and patterns in the data.

We compare the performance of the HydroLSTM and LSTM architectures using data from 10 hydro-climatically varied catchments. We further examine how the new architecture exploits the information in lagged inputs, for 588 catchments across the USA. The HydroLSTM-based models require fewer cell states to obtain similar performance to their LSTM-based counterparts. Further, the weights patterns associated with lagged input variables are interpretable and consistent with regional hydroclimatic characteristics (snowmelt-dominated, recent rainfall-dominated, and historical rainfall-dominated). These findings illustrate how the hydrological interpretability of LSTM-based models can be enhanced by appropriate architectural modifications that are physically and conceptually consistent with our understanding of the system.

Luis Andres De la Fuente et al.

Status: open (until 08 Jul 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on egusphere-2023-666', Grey Nearing, 22 Apr 2023 reply
    • AC1: 'Reply on CC1', Luis De La Fuente, 29 Apr 2023 reply
  • RC1: 'Comment on egusphere-2023-666', Tadd Bindas, 30 May 2023 reply

Luis Andres De la Fuente et al.

Luis Andres De la Fuente et al.


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Short summary
Long Short-Term Memory (LSTM) is a widely-used machine learning (ML) model in hydrology. However, it is difficult to extract knowledge from it. We propose HydroLSTM which represents processes analogous to a hydrological reservoir. Models using HydroLSTM perform similarly to LSTM but require fewer cell states. The learned parameters are informative about the dominant hydroclimatic characteristics of a catchment. Our results demonstrate how hydrological knowledge is encoded in the new structure.