Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 5 Model predictions
Dates
Publication Date
2020-12-09
Start Date
2010-10-01
End Date
2016-09-30
Citation
Rahmani, F., Lawson, K., Ouyang, W., Appling, A.P., Oliver, S.K., and Shen, C., 2020, Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: U.S. Geological Survey data release, https://doi.org/10.5066/P97CGHZH.
Summary
This data release component contains water temperature predictions in 118 river catchments across the U.S. Predictions are from the four models described by Rahmani et al. (2020): locally-fitted linear regression, LSTM-noQ, LSTM-obsQ, and LSTM-simQ.
Summary
This data release component contains water temperature predictions in 118 river catchments across the U.S. Predictions are from the four models described by Rahmani et al. (2020): locally-fitted linear regression, LSTM-noQ, LSTM-obsQ, and LSTM-simQ.
