Articles | Volume 25, issue 5
Hydrol. Earth Syst. Sci., 25, 2951–2977, 2021
https://doi.org/10.5194/hess-25-2951-2021
Hydrol. Earth Syst. Sci., 25, 2951–2977, 2021
https://doi.org/10.5194/hess-25-2951-2021

Research article 31 May 2021

Research article | 31 May 2021

Machine-learning methods for stream water temperature prediction

Moritz Feigl et al.

Data sets

MoritzFeigl/wateRtemp: HESS submission Moritz Feigl https://doi.org/10.5281/zenodo.4438575

Model code and software

MoritzFeigl/ML_methods_for_stream_water_temperature_prediction: HESS paper Moritz Feigl https://doi.org/10.5281/zenodo.4438582

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Short summary
In this study we developed machine learning approaches for daily river water temperature prediction, using different data preprocessing methods, six model types, a range of different data inputs and 10 study catchments. By comparing to current state-of-the-art models, we could show a significant improvement of prediction performance of the tested approaches. Furthermore, we could gain insight into the relationships between model types, input data and predicted stream water temperature.