Hydroclimatic streamflow prediction using Least Square-Support Vector Regression

In last two decades, many Artificial Intelligence-based models have gained recognition in the field of hydrologic forecasting. In this study, potential of least square support vector regression model is explored in the context of streamflow prediction using hydroclimatic inputs. This study is conducted in Narmada river basin up to Sandia gauging station and Mahanadi river basin up to Basantpur gauging station. Four different hydroclimatic variables, namely rainfall, maximum temperature, minimum temperature and streamflow values of previous day are used as input variables. Prediction performances are assessed in terms of different statistical measures, namely – correlation coefficient, root mean square error and Nash–Sutcliffe efficiency. Whereas these statistics indicate an overall impressive result, in particular, low and medium ranges of streamflows are found to have better correspondence between observed and predicted values.