Skip to main content
Advanced Search

Filters: partyWithName: Wenyu Ouyang (X)

7 results (8ms)   

Filters
Date Range
Contacts
Categories
Tag Types
Tag Schemes
View Results as: JSON ATOM CSV
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.
This data release component contains evaluation metrics used to assess the predictive performance of each stream temperature model. For further description, see the metric calculations in the supplement of Rahmani et al. (2020), equations S1-S7.
This data release component contains mean daily stream water temperature observations, retrieved from the USGS National Water Information System (NWIS) and used to train and validate all temperature models. The model training period was from 2010-10-01 to 2014-09-30, and the test period was from 2014-10-01 to 2016-09-30.
This data release provides all data and code used in Rahmani et al. (2020) to model stream temperature and assess results. Briefly, we used a subset of the USGS GAGES-II dataset as a test case for temperature prediction using deep learning methods. The associated manuscript explores the value of including stream discharge as a predictor in the temperature models, including the value of predicted discharge from a separate model when no discharge measurements are available. The data are organized into these items: Spatial Information - Locations of the 118 monitoring sites used in this study Observations - Water temperature observations for the 118 sites used in this study Model Inputs - Model inputs, including basin...
    map background search result map search result map Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 1 Spatial information Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 2 Observations Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 3 Model inputs Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 4 Models Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 5 Model predictions Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 6 Model evaluation Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 1 Spatial information Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 2 Observations Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 3 Model inputs Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 4 Models Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 5 Model predictions Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data: 6 Model evaluation