Abstract
The main objective of this study is to derive a flexible approach based on machine learning techniques, i.e. Support Vector Regression (SVR), for monthly river discharge forecasting with 1-month lead time. The proposed approach has been tested over 300 alpine basins, in order to explore advantages and limits in an operational perspective. The main relevant input features in the forecast performances are the snow cover areas and the discharge behavior of the previous years. Forecasts obtained by training SVR machine on single gauging stations show better performances than the average of the previous 10 years, considered as benchmark, in 94% of the cases, with a mean improvement of about 48% in root mean square error. In case of poorly gauged basins, to increase the number of training sample, multiple basins have been considered to train the SVR machine. In this case, performances are still better than the benchmark, even if worse than those of SVR machine trained on single basins, with a decrease of the performances ranging from 13% to 54%.
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Acknowledgments
The research was conducted with support awarded to GECOsistema, R&D Unit Sudtirol, by the Province of Bolzano (LP 14/2006, “Bando Innovazione” 2011), in collaboration with EURAC Research.
We acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://ensembles-eu.metoffice.com) and the data providers in the ECA&D project (http://www.ecad.eu).
The data used in this study [GLDAS] were acquired as part of the mission of NASA’s Earth Science Division and archived and distributed by the Goddard Earth Sciences (GES) Data and Information Services Center (DISC).
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De Gregorio, L., Callegari, M., Mazzoli, P. et al. Operational River Discharge Forecasting with Support Vector Regression Technique Applied to Alpine Catchments: Results, Advantages, Limits and Lesson Learned. Water Resour Manage 32, 229–242 (2018). https://doi.org/10.1007/s11269-017-1806-3
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DOI: https://doi.org/10.1007/s11269-017-1806-3