HIGHLIGHTS
- who: Hydrol. Earth Syst. Sci. and collaborators from the Systems Science Division, Pacific Northwest National Laboratory, Richland, WA, USA , Department of and Atmospheric Sciences, Indiana University, Bloomington, IN, USA have published the research: Technical note: Using long short-term memory models to fill data gaps in hydrological monitoring networks, in the Journal: (JOURNAL)
- what: The authors explore the ability of recurrent neural networks to fill gaps in a spatially distributed time-series dataset. The study demonstrates that the ARIMA models yield better average error statistics although they tend to have larger errors during time windows . . .
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