Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 25, issue 6
Articles | Volume 25, issue 6
https://doi.org/10.5194/hess-25-3017-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-3017-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 6
Research article
 | 
04 Jun 2021
Research article |  | 04 Jun 2021

Investigating ANN architectures and training to estimate snow water equivalent from snow depth

Konstantin F. F. Ntokas, Jean Odry, Marie-Amélie Boucher, and Camille Garnaud

Data sets

Replication Data for the manuscript "Using an ensemble of artificial neural networks to convert snow depth to snow water equivalent over Canada" submitted to HESS Marie-Amelie Boucher https://doi.org/10.7910/DVN/T46ANR

Model code and software

Hydrology_ANN_SD2SWE (Code of the project) Konstantin Franz Fotios Ntokas https://github.com/konstntokas/Hydrology_ANN_SD2SWE

konstntokas/Hydrology_ANN_SD2SWE: Publication HESS (Version v1.0) Konstantin Ntokas https://doi.org/10.5281/zenodo.4276414

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Short summary
This article shows a conversion model of snow depth into snow water equivalent (SWE) using an ensemble of artificial neural networks. The novelty is a direct estimation of SWE and the improvement of the estimation by in-depth analysis of network structures. The usage of an ensemble allows a probabilistic estimation and, therefore, a deeper insight. It is a follow-up study of a similar study over Quebec but extends it to the whole area of Canada and improves it further.
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