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Seasonal river discharge forecasting using support vector regression: A case study in the Italian Alps

TitoloSeasonal river discharge forecasting using support vector regression: A case study in the Italian Alps
Tipo di pubblicazioneArticolo su Rivista peer-reviewed
Anno di Pubblicazione2015
AutoriCallegari, M., Mazzoli P., de Gregorio L., Notarnicola C., Pasolli L., Petitta Marcello, and Pistocchi A.
RivistaWater (Switzerland)
Volume7
Paginazione2494-2515
ISSN20734441
Parole chiavealpine environment, Alps, Alto Adige, antecedent conditions, Balloons, catchment, Catchments, climate variation, Errors, Forecasting, Hydrological forecast, hydrological modeling, Italy, linearity, Mean square error, Meteorology, prediction, Regression, Regression analysis, River discharge, Runoff, seasonal variation, Snow, snow cover, Snow cover area, South Tyrol, support vector machine, Support vector machines, Trentino-Alto Adige, Weather forecasting
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. © 2015 by the authors.

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URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84938677410&doi=10.3390%2fw7052494&partnerID=40&md5=2d05df5487e128d47a5a9ab6bc31f20f
DOI10.3390/w7052494
Citation KeyCallegari20152494