Stream Flow Forecasting Using Least Square Support Vector Regression

Accurate forecasting of streamflow for different lead-times is useful in the design of almost all hydraulic structures. The Support Vector Machines (SVMs) use a hypothetical space of linear functions in a kernel-induced higher dimensional feature space and are trained with a learning algorithm from optimization theory. The support vector regression attempts to fit a curve on data points such that the points lie between two marginal hyperplanes which will minimize the error. The current paper presents least square support vector regression (LS-SVR) to predict one day ahead stream flow using past values of the rainfall and river flow at three stations in India, namely Nighoje and Budhwad in Krishna river basin and Mandaleshwar in Narmada river basin. The relevant inputs are finalized on the basis of three techniques namely autocorrelation, Cross-correlation and trial and error. The forecasting model results are reasonable as can be seen from a low value of Root Mean Square Error (RMSE), Mean Absolute Relative Error (MARE) and high values of Coefficient of Efficiency (CE) accompanied by balanced scatter plots and hydrographs.

Abstract licence: CC BY

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.