Supporting data for publication (Prediction of flow duration curve for ungauged basins: Machine learning and deep learning approach)
| Authors: | |
|---|---|
| Owners: | Sooyeon Yi |
| Type: | Resource |
| Storage: | The size of this resource is 509.4 KB |
| Created: | Oct 24, 2024 at 2:10 a.m. |
| Last updated: | Oct 28, 2024 at 12:47 p.m. (Metadata update) |
| Published date: | Oct 28, 2024 at 12:47 p.m. |
| DOI: | 10.4211/hs.127d1b944fce46caaa89bac236627ed4 |
| Citation: | See how to cite this resource |
| Sharing Status: | Published |
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| Views: | 301 |
| Downloads: | 6 |
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Abstract
The flow duration curve characterizes streamflow variability, crucial for river management. Constructing FDCs is challenging in areas without gauging stations. This study explores machine learning and deep learning models, including random forest, deep neural network, support vector regression, and elastic net regression, to predict FDCs in ungauged basins. Using data from streamflow stations, we predict streamflow percentiles . The models utilize various combinations of accumulated precipitation and topographic features to improve prediction accuracy.
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How to Cite
Yi, S. (2024). Supporting data for publication (Prediction of flow duration curve for ungauged basins: Machine learning and deep learning approach), HydroShare, https://doi.org/10.4211/hs.127d1b944fce46caaa89bac236627ed4
This resource is shared under the Creative Commons Attribution CC BY.
http://creativecommons.org/licenses/by/4.0/
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