Published February 14, 2023
| Version v1
Software
Open
Data and Code: In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance
Creators
- 1. Institute for Machine Learning, Johannes Kepler University, Linz, Austria
- 2. Google Research
- 3. Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, USA
- 4. Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, Canada
Description
This repository contains the code and data for the study "In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance".
This zenodo submission archives the GitHub repository that is available at https://github.com/gauchm/rate-my-hydrograph
Files
rate-my-hydrograph-main.zip
Files
(63.2 MB)
| Name | Size | Download all |
|---|---|---|
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md5:3acc71da34b58dae4490c64a1b349214
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63.2 MB | Preview Download |
Additional details
Related works
- Is supplement to
- Preprint: 10.31223/X52938 (DOI)