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Water 2015, 7(5), 2494-2515; doi:10.3390/w7052494

Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps

1
EURAC Research, European Academy of Bozen/Bolzano, Institute for Applied Remote Sensing, viale Druso, Bolzano 1-39100, Italy
2
Department of Earth and Environmental Science, University of Pavia, via Ferrata 1-27100 Pavia, Italy
3
Research and development (R&D) Unit Suedtirol, Geographic Environmental COnsulting (GECO) Sistema—srl, via Maso della Pieve 60, Bolzano 39100, Italy
4
Informatica Trentina Spa, via G. Gilli 2, Trento 38121, Italy
5
European Commission, Directorate-General Joint Research Centre (DG JRC), via E.Fermi 2749, Ispra (VA) 21027, Italy
*
Author to whom correspondence should be addressed.
Academic Editors: Roger Falconer and Clelia Marti
Received: 25 February 2015 / Accepted: 11 May 2015 / Published: 22 May 2015
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Abstract

In this contribution we analyze the performance of a monthly river discharge forecasting model with a Support Vector Regression (SVR) technique in a European alpine area. We considered as predictors the discharges of the antecedent months, snow-covered area (SCA), and meteorological and climatic variables for 14 catchments in South Tyrol (Northern Italy), as well as the long-term average discharge of the month of prediction, also regarded as a benchmark. Forecasts at a six-month lead time tend to perform no better than the benchmark, with an average 33% relative root mean square error (RMSE%) on test samples. However, at one month lead time, RMSE% was 22%, a non-negligible improvement over the benchmark; moreover, the SVR model reduces the frequency of higher errors associated with anomalous months. Predictions with a lead time of three months show an intermediate performance between those at one and six months lead time. Among the considered predictors, SCA alone reduces RMSE% to 6% and 5% compared to using monthly discharges only, for a lead time equal to one and three months, respectively, whereas meteorological parameters bring only minor improvements. The model also outperformed a simpler linear autoregressive model, and yielded the lowest volume error in forecasting with one month lead time, while at longer lead times the differences compared to the benchmarks are negligible. Our results suggest that although an SVR model may deliver better forecasts than its simpler linear alternatives, long lead-time hydrological forecasting in Alpine catchments remains a challenge. Catchment state variables may play a bigger role than catchment input variables; hence a focus on characterizing seasonal catchment storage—Rather than seasonal weather forecasting—Could be key for improving our predictive capacity. View Full-Text
Keywords: seasonal hydrological forecast; snow cover area; support vector machine; regression; South Tyrol; Alps seasonal hydrological forecast; snow cover area; support vector machine; regression; South Tyrol; Alps
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Callegari, M.; Mazzoli, P.; de Gregorio, L.; Notarnicola, C.; Pasolli, L.; Petitta, M.; Pistocchi, A. Seasonal River Discharge Forecasting Using Support Vector Regression: A Case Study in the Italian Alps. Water 2015, 7, 2494-2515.

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