Streamflow prediction using a hybrid methodology based on variational mode decomposition (VMD) and machine learning approaches

Abstract

Abstract The optimal management of water resources depends on accurate and reliable streamflow prediction. Therefore, researchers have become interested in the development of hybrid approaches in recent years to enhance the performance of modeling techniques for predicting hydrological variables. In this study, hybrid models based on variational mode decomposition (VMD) and machine learning models such as random forest (RF) and K-star algorithm (KS) were developed to improve the accuracy of streamflow forecasting. The monthly data obtained between 1956 and 2017 at the Iranian Bibijan Abad station on the Zohreh River were used for this purpose. The streamflow data were initially decomposed into intrinsic modes functions (IMFs) using the VMD approach up to level eight to develop the hybrid models. The following step models the IMFs obtained by the VMD approach using the RF and KS methods. The ensemble forecasting result is then accomplished by adding the IMFs’ forecasting outputs. Other hybrid models, such as EDM-RF, EMD-KS, CEEMD-RF, and CEEMD-KS, were also developed in this research in order to assess the performance of VMD-RF and VMD-KS hybrid models. The findings demonstrated that data preprocessing enhanced standalone models’ performance, and those hybrid models developed based on VMD performed best in terms of increasing the accuracy of monthly streamflow predictions. The VMD-RF model is proposed as a superior method based on root mean square error (RMSE = 13.79), mean absolute error (MAE = 8.35), and Kling–Gupta (KGE = 0.89) indices.

Keywords

• Central frequency
• Decomposition level
• Noise
• Sub-series