Prediction of River Stage-Discharge Process Based on a Conceptual Model Using EEMD-WT-LSSVM Approach

Abstract

This study proposed a hybrid pre-processing approach along with a conceptual model to enhance the accuracy of river discharge prediction. In order to achieve this goal, Ensemble Empirical Mode Decomposition algorithm (EEMD), Wavelet transform (WT) and Mutual Information (MI) were employed as a hybrid pre-processing approach conjugated to Least Square Support Vector Machine (LSSVM). For this end, a conceptual strategy (multi-station model) was developed to forecast the Souris River discharge more accurately. The strategy used herein was capable of covering uncertainties and complexities of river discharge modeling. At first a classic model along with WT was performed to predict the one-day-ahead river discharge for the each single station. Based on the results, db4 and decomposition level equal to 5 (db(4,5)) presented a better performance among selected mother wavelets for all hydrometric station. Therefore db(4,5) and EEMD was coupled and feature selection was performed for decomposed sub-series using MI to be employed in conceptual models. In the proposed feature selection method, some useless sub-series were omitted to achieve better performance. Results approved efficiency of the proposed WT-EEMD-MI approach to improve accuracy of different modeling strategies.