Suspended sediment load prediction in consecutive stations of river based on ensemble pre-post-processing kernel based approaches Roghayeh Ghasempour, Kiyoumars Roushangar and Parveen Sihag ABSTRACT Sediment transportation and accurate estimation of its rate is a significant issue for river engineers and researchers. In this study, the capability of kernel based approaches including Kernel Extreme Learning Machine (KELM) and Gaussian Process Regression (GPR) was assessed for predicting the river daily Suspended Sediment Discharge (SSD). For this aim, the Mississippi river, with three consecutive hydrometric stations, was selected as the case study. Based on the sediment and flow characteristics during the period of 2005–2008, several models were developed and tested under two scenarios (i.e. modeling based on each station’s own data or the previous stations’ data). Two post-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used for enhancing the SSD modeling capability. Also, data post- proceeding was done using Simple Linear Averaging (SLAM) and Nonlinear Kernel Extreme Learning Machine Ensemble (NKELME) methods. Obtained results indicated that the integrated models resulted in more accurate outcomes. Data processing enhanced the models’ capability up to 35%. It was found that SSD modeling based on the station’s own data led to better results; however, using the integrated approaches, the previous station’s data could be applied successfully for the SSD modeling when a station’s own data were not available. Key words | EEMD, GPR, pre-processing, successive stations, suspended sediment load HIGHLIGHTS • Merge the advantages of the pre-post-processing and kernel based techniques for suspended sediment discharge prediction. • Two states of modeling based on a station’s own data or the previous stations’ data were investigated. • Integrated hybrid techniques outperformed the single meta-model approaches. Roghayeh Ghasempour (corresponding author) Kiyoumars Roushangar Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran E-mail: ghassempourroghy@gmail.com Parveen Sihag Department of Civil Engineering, Shoolini University, Solan, Himachal Pradesh, India This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY-NC-ND 4.0), which permits copying and redistribution for non-commercial purposes with no derivatives, provided the original work is properly cited (http://creativecommons.org/ licenses/by-nc-nd/4.0/). 1 © 2021 The Authors Water Supply | in press | 2021 doi: 10.2166/ws.2021.094 Corrected Proof Downloaded from http://iwaponline.com/ws/article-pdf/doi/10.2166/ws.2021.094/874668/ws2021094.pdf by guest on 01 May 2021