Articles | Volume 26, issue 7
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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Cited articles

Alvera-Azcárate, A., Barth, A., Parard, G., and Beckers, J.-M.: Analysis of SMOS sea surface salinity data using DINEOF, Remote Sens Environ., 180, 137–145, 2016. a
Amaranto, A., Munoz-Arriola, F., Corzo, G., Solomatine, D. P., and Meyer, G.: Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland, J. Hydroinform., 20, 1227–1246, 2018. a
Amaranto, A., Munoz-Arriola, F., Solomatine, D., and Corzo, G.: A spatially enhanced data-driven multimodel to improve semiseasonal groundwater forecasts in the High Plains aquifer, USA, Water Resour. Res., 55, 5941–5961, 2019. a
Banerjee, S., Carlin, B. P., and Gelfand, A. E.: Hierarchical modeling and analysis for spatial data, CRC Press,, 2014. a
Beckers, J.-M. and Rixen, M.: EOF calculations and data filling from incomplete oceanographic datasets, J. Atmos. Ocean. Tech., 20, 1839–1856, 2003. a
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.