The prediction of monthly mean discharge is critical for water resources management. Statistical methods applied on discharge time series are traditionally used for predicting this kind of slow response hydrological events. With this paper we present a Support Vector Regression (SVR) system able to predict monthly mean discharge considering discharge and snow cover extent (250 meters resolution obtained by MODIS images) time series as inputs. Additional meteorological and climatic variables are also tested as inputs for the SVR approach. The prediction system has been evaluated on 14 catchments in South Tyrol (Northern Italy). Considering as a reference the estimates based on the average discharge computed on the past 10 years, which is a common practice for water resources management in the study region, the percentage root mean square error (RMSE%) is reduced of 11% and 6% for a prediction lag of 1 and 3 months respectively.

Seasonal river discharge forecast in Alpine catchments using snow map time series and support vector regression approach

CALLEGARI, MATTIA;SEPPI, ROBERTO;
2014

Abstract

The prediction of monthly mean discharge is critical for water resources management. Statistical methods applied on discharge time series are traditionally used for predicting this kind of slow response hydrological events. With this paper we present a Support Vector Regression (SVR) system able to predict monthly mean discharge considering discharge and snow cover extent (250 meters resolution obtained by MODIS images) time series as inputs. Additional meteorological and climatic variables are also tested as inputs for the SVR approach. The prediction system has been evaluated on 14 catchments in South Tyrol (Northern Italy). Considering as a reference the estimates based on the average discharge computed on the past 10 years, which is a common practice for water resources management in the study region, the percentage root mean square error (RMSE%) is reduced of 11% and 6% for a prediction lag of 1 and 3 months respectively.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11571/942434
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