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Deep learning approaches for improving prediction of daily stream temperature in data-scarce, unmonitored, and dammed basins

Dates

Publication Date
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 provides all data and code used in Rahmani et al. (2021b) to model stream temperature and assess results. Briefly, we modeled stream temperature at sites across the continental United States using deep learning methods. The associated manuscript explores the prediction challenges posed by reservoirs, the value of additional training sites when predicting in gaged vs ungaged sites, and the value of an ensemble of attribute subsets in improving prediction accuracy. The data are organized into these child items: Site Information - Attributes and spatial information about the monitoring sites and basins in this study Observations - Water temperature observations for the sites used in this study Model Inputs - Model input, [...]

Child Items (5)

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Purpose

Water quality research, advancement of machine learning in hydrology, improve predictions of stream temperature in ungagged or dammed basins.

Additional Information

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Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P9VHMO56

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