Articles | Volume 26, issue 7
https://doi.org/10.5194/hess-26-1727-2022
https://doi.org/10.5194/hess-26-1727-2022
Technical note
 | 
05 Apr 2022
Technical note |  | 05 Apr 2022

Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks

Huiying Ren, Erol Cromwell, Ben Kravitz, and Xingyuan Chen

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Latest update: 18 May 2024
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Short summary
We used a deep learning method called long short-term memory (LSTM) to fill gaps in data collected by hydrologic monitoring networks. LSTM accounted for correlations in space and time and nonlinear trends in data. Compared to a traditional regression-based time-series method, LSTM performed comparably when filling gaps in data with smooth patterns, while it better captured highly dynamic patterns in data. Capturing such dynamics is critical for understanding dynamic complex system behaviors.