Data and Code: In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 14 Feb 2023 English Publisher:ZenodoFunded by:FWF | AI-Based Retinal Image An..., EC | ELISE

Authors: Gauch, Martin; Kratzert, Frederik; Gilon, Oren; Gupta, Hoshin; Mai, Juliane; Nearing, Grey; Tolson, Bryan; +2 Authors

doi: 10.5281/zenodo.7640347 , 10.5281/zenodo.7640348

Data and Code: In Defense of Metrics: Metrics Sufficiently Encode Typical Human Preferences Regarding Hydrological Model Performance

- Summary
- Subjects
- Metrics

Abstract

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

Related Organizations

Johannes Kepler University of Linz
Austria
University of Waterloo
Canada
University of Arizona
United States

Keywords

metrics, machine learning, hydrology, visual inspection, rainfall-runoff, expert judgment

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