4 Model Code: Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins
Dates
Publication Date
2021-09-27
Start Date
2010-10-01
End Date
2016-09-30
Citation
Rahmani, F., Shen, C., Oliver, S.K., Lawson, K., David Watkins, and Appling, A.P., 2021, Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins: U.S. Geological Survey data release, https://doi.org/10.5066/P9VHMO56.
Summary
This data release component contains model code and configurations for the LSTM models used to predict stream temperature.
Summary
This data release component contains model code and configurations for the LSTM models used to predict stream temperature.
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04_model_code.xml Original FGDC Metadata
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models.zip
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Related External Resources
Type: Related Primary Publication
Rahmani, F., Shen, C., Oliver, S., Lawson, K. and Appling, A. (2021), Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins. Hydrological Processes. https://doi.org/10.1002/hyp.14400
