Performance comparison of machine learning models for streamflow forecasting

In this work, the performance of three models for streamflow forecasting is compared, based on six scenarios that considered the phases of El Niño South Oscillation (ENSO) and Climate Indicators, for eight hydroelectric plants located in four regions of the Brazilian territory. The models addressed are Support Vector Regression, Extreme Learning Machine and Kernel Ridge Regression. The climatic variables used are the rainfall, the location of the Intertropical Convergence Zone (ITCZ) and occurrence data from the South Atlantic Convergence Zone (SACZ). The criterion for comparing the models considered the means and variances of the series of forecast errors for each Plant. The computational results indicated that the Kernel Ridge Regression model obtained the best results in most of the tested scenarios, including those that considered the use of Climate Indicators.