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Data and model code used to evaluate a process-guided deep learning approach for in-stream dissolved oxygen prediction
This model archive contains data and code used to assess the use of process-informed multi-task deep learning models for predicting in-stream dissolved oxygen concentrations. Three holdout experiments were run to assess model performance, including a temporal holdout experiment, a spatial holdout experiment with similar sites held out, and a spatial holdout experiment with dissimilar sites held out. This model archive includes data from 10 sites in the lower Delaware River Basin that were used in the model experiments. Model training target data include dissolved oxygen concentrations downloaded from the National Water Information System (NWIS) (U.S. Geological Survey 2023). Model input data include daily meteorological driver variables derived from gridded surface data (gridMET; Abatzoglou 2013); river and catchment characteristics (Wieczorek et al. 2018); and estimates of daily stream metabolism rates (Appling et al. 2018). The contents of this model archive are organized into files or file directories that have been aggregated into zip files:
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"010:12"
]
|
| contactPoint |
{
"fn": "Jeffrey M. Sadler",
"@type": "vcard:Contact",
"hasEmail": "mailto:jsadler@usgs.gov"
}
|
| description | This model archive contains data and code used to assess the use of process-informed multi-task deep learning models for predicting in-stream dissolved oxygen concentrations. Three holdout experiments were run to assess model performance, including a temporal holdout experiment, a spatial holdout experiment with similar sites held out, and a spatial holdout experiment with dissimilar sites held out. This model archive includes data from 10 sites in the lower Delaware River Basin that were used in the model experiments. Model training target data include dissolved oxygen concentrations downloaded from the National Water Information System (NWIS) (U.S. Geological Survey 2023). Model input data include daily meteorological driver variables derived from gridded surface data (gridMET; Abatzoglou 2013); river and catchment characteristics (Wieczorek et al. 2018); and estimates of daily stream metabolism rates (Appling et al. 2018). The contents of this model archive are organized into files or file directories that have been aggregated into zip files: |
| distribution |
[
{
"@type": "dcat:Distribution",
"title": "Digital Data",
"format": "XML",
"accessURL": "https://doi.org/10.5066/P13T6EYN",
"mediaType": "application/http",
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{
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"description": "The metadata original format",
"downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.649600a6d34ef77fcb01e736.xml"
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|
| identifier | http://datainventory.doi.gov/id/dataset/USGS_649600a6d34ef77fcb01e736 |
| keyword |
[
"US",
"USGS:649600a6d34ef77fcb01e736",
"United States",
"deep learning",
"dissolved oxygen",
"environment",
"hybrid modeling",
"inlandWaters",
"machine learning",
"modeling",
"streams",
"water",
"water resources"
]
|
| modified | 2024-09-23T00:00:00Z |
| publisher |
{
"name": "U.S. Geological Survey",
"@type": "org:Organization"
}
|
| spatial | -76.39556, 39.5, -74.37121, 40.89106 |
| theme |
[
"Geospatial"
]
|
| title | Data and model code used to evaluate a process-guided deep learning approach for in-stream dissolved oxygen prediction |
