A novel approach for prediction of daily streamflow discharge data using correlation based feature selection and random forest method

Abstract

The accurate methods for the forecasting of hydrological characteristics are significantly important for water resource management and environmental aspects. In this study, a novel approach for daily streamflow discharge data forecasting is proposed. Streamflow discharge, temperature, and precipitation data were used for feature extraction which were systematically employed for forecasting studies. While the correlation-based feature selection (CFS) was used for feature selection, Random Forest (RF) model is employed for forecasting of following 7 days. Moreover, an accuracy comparison between the RF model and CFS-RF model is drawn by using streamflow discharge data. Acquired results confirmed the accuracy of CFS-RF model for both, middle and extended forecasting times compared to RF model which had similar accuracy values for the closer forecasting times. Moreover, the CFS-RF model proved to be much robust for extended forecasting durations.