Metamorphic testing of machine learning and conceptual hydrologic models

Reichert, Peter; Ma, Kai; Höge, Marvin; Fenicia, Fabrizio; Baity-Jesi, Marco; Feng, Dapeng; Shen, Chaopeng

doi:https://doi.org/10.5194/hess-28-2505-2024

Articles | Volume 28, issue 11

https://doi.org/10.5194/hess-28-2505-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-28-2505-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 28, issue 11

Research article

|

13 Jun 2024

Research article |

| 13 Jun 2024

Metamorphic testing of machine learning and conceptual hydrologic models

Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen

Supplement

https://doi.org/10.5194/hess-28-2505-2024-supplement

Data sets

A large-sample watershed-scale hydrometeorological dataset for the contiguous USA A. Newman et al. https://doi.org/10.5065/D6MW2F4D

Model code and software

Data for: Metamorphic Testing of Machine Learning and Conceptual Hydrologic Models P. Reichert et al. https://doi.org/10.25678/000CQ0

MHPI-hydroDL C. Shen https://doi.org/10.5281/zenodo.3993880

Short summary

We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.