Operational River Discharge Forecasting with Support Vector Regression Technique Applied to Alpine Catchments: Results, Advantages, Limits and Lesson Learned

Ludovica Gregorio (), Mattia Callegari (), Paolo Mazzoli (), Stefano Bagli (), Davide Broccoli (), Alberto Pistocchi () and Claudia Notarnicola ()
Additional contact information
Ludovica Gregorio: Institute for Earth Observation
Mattia Callegari: Institute for Earth Observation
Paolo Mazzoli: Research and development (R&D) Unit Suedtirol, Geographic Environmental COnsulting (GECO)
Stefano Bagli: Research and development (R&D) Unit Suedtirol, Geographic Environmental COnsulting (GECO)
Davide Broccoli: Research and development (R&D) Unit Suedtirol, Geographic Environmental COnsulting (GECO)
Alberto Pistocchi: European Commission, Directorate-General Joint Research Centre (DG JRC)
Claudia Notarnicola: Institute for Earth Observation

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2018, vol. 32, issue 1, No 13, 229-242

Abstract: 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%.

Keywords: Seasonal discharge forecast; Support vector regression; Snow cover area; Machine learning; Alpine Arc (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11269-017-1806-3

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