Application of boosted tree algorithm with new data preprocessing techniques in the forecasting one day ahead streamflow values in the Tigris basin, Türkiye

Abstract

Accurate streamflow forecasting is very useful in water resources management, design of hydraulic structures, and almost all issues related to the use of water and water resources, especially in arid regions that have increased in recent years. Since water is the source of all life and the most important basic element for humanity to continue its life, streamflow prediction studies increase its importance daily. This research combined the boosted tree (BT) model with robust empirical mode decomposition, empirical mode decomposition, complete ensemble empirical mode decomposition with adaptive noise, empirical wavelet transforms and variational mode decomposition for predicting daily average streamflow data. While historical streamflow data was input in the model's setup, one-day lead-time streamflow data was used as the target. 70% of the data is reserved for training and the rest for testing. 5-fold cross-validation technique was used to solve the over-fitting problem. The coefficient of determination, mean squared error, Nash-Sutcliffe efficiency and percent bias statistical criteria and Taylor diagrams, polar plot, scattering diagram, and violin plot were used to determine the algorithm's success. At the end of the study, it was found that the most successful streamflow predictions were made with the variational mode decomposition-based BT hybrid approach.