Empirical mode decomposition coupled with least square support vector machine for river flow forecasting

Abstract

This paper aims to investigate the ability of Empirical Mode Decompositio n (EMD) coupled with Least Square Support Vector Machine (LSSVM) model in order to improve the accuracy of river flow forecasting. To assess the effectiveness of this model, Bernam monthly river flow data, has served as the case study. The proposed model was set at three important stages which are decomposition, component identification and forecasting stages respectively. The first stage is known as decomposition stage where EMD were employed for decomposing the dataset into several numbers of Intrinsic Mode Functions (IMF) and a residue. During on second stage, the meaningful signals are identified using a statistical measure and the new dataset are obtained in this stage. The final stage applied LSSVM as a forecasting tool to perform the river flow forecasting. The performance of the EMD coupled with LSSVM model is compared with the single LSSVM models using various statistics measures of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), correlation-coefficient (R) and Correlation of Efficiency (CE). The comparison results reveal the proposed model of EMD coupled with LSSVM model serves as a useful tool and a promising new method for river flow forecasting.