Forecasting One Day Ahead Stream Flow Using Support Vector Regression

Abstract

Effective stream flow forecast for different lead-times is useful in water resource management in arid regions, in designing of hydraulic structures and almost all water resources related issues. The Support Vector Machines are learning systems that 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. Support vector machines are the methods of supervised learning, which are commonly used for classification and regression purpose. A SVM constructs a separating hyper plane between the classes in the n-dimensional space of the inputs. The Support Vector Regression attempts to fit a curve with respect to the kernel used in SVM on data points such that the points lie between two marginal hyper planes which helps in minimizing the regression error. For non-linear regression problems Kernel functions are used to map the data into higher dimensional space where linear regression is performed. The current paper presents use of a data driven technique of Support Vector Regression (SVR) to forecast stream flow one day ahead at two stations in India, namely Nighoje in Krishna river basin and another station is Mandaleshwar in Narmada river basin. For forecasting stream flow one day in advance previous values of measured stream flow and rainfall were used for building the models. The relevant inputs were fixed on the basis of autocorrelation, Cross-correlation and trial and error. The model results were reasonable as evident from low value of Root Mean Square Error (RMSE) accompanied by scatter plots and hydrographs.