A two-stage multiple-point conceptual model to predict river stage-discharge process using machine learning approaches

Abstract

Due to the complex nature of river stage-discharge process, the present study tried to develop a unique strategy to predict it precisely. The proposed conceptual strategy has some advantages to cover the shortcomings. First, it uses one model instead of several models to predict multiple points instead of one point. On the one hand, the constructed model was inspired by physical-based model (to include time-space attributes of the catchment). On the other hand, ensemble empirical mode decomposition algorithm (EEMD), wavelet transform (WT), and mutual information (MI) were employed as a hybrid pre-processing approach conjugated to support vector machine. 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. First, a classic model along with WT was performed to predict the 1-day-ahead river discharge for each single station. Therefore DWT-EEMD and feature selection were used for decomposed subseries using MI to be employed in conceptual models. In the proposed feature selection method, some useless subseries were deleted to achieve better performance. The results approved efficiency of the proposed WT-EEMD-MI approach to improve accuracy of different modeling strategies.

Keywords

• ensemble empirical decomposition mode (eemd)
• machine learning
• multi-station modeling
• mutual information (mi)
• river stage–discharge process
• wavelet transform (wt)