Sediment transport is one of the most important issues in river engineering. In this study, the capability of the Kernel Extreme Learning Machine (KELM) approach for predicting the river daily Suspended Sediment Concentration (SSC) and Discharge (SSD) was assessed. Three successive hydrometric stations of Mississippi river were considered and based on the sediment and flow characteristics during the period of 2005–2008. Several models were developed and tested for SSC and SSD modeling. For improving the applied model efficiency, two post-processing techniques, namely Wavelet Transform (WT) and Ensemble Empirical Mode Decomposition (EEMD), were used. Also, two states of modeling based on stations' own data (state 1) and previous stations' data (state 2) were considered. The single and integrated KELM model results comparison indicated that the integrated WT and EEMD-KELM models resulted in more accurate outcomes. Results showed that data processing with WT was more effective than EEMD in increasing the models' efficiency. Data processing enhanced the models' capability by up to 15%. The results showed that the state 1 modeling led to better results, however, using the integrated KELM approaches the previous stations data could be applied successfully for SSC and SSD modeling when the stations' own data were not available.

HIGHLIGHT

• The suspended sediment concentration (SSC) and suspended sediment discharge (SSD) were predicted via artificial intelligence approach in successive hydrometric stations. The data pre-processing impacts on models' efficiency improvement was assessed. The sensitivity analysis showed the most effective subseries was obtained from pre-processing models.

泥沙输送是河流工程中最重要的问题之一。在这项研究中，评估了内核极限学习机（KELM）方法预测河流每日悬浮沉积物浓度（SSC）和流量（SSD）的能力。根据2005-2008年期间的沉积物和流量特征，考虑了密西西比河的三个连续的水文站。开发了几种模型，并针对SSC和SSD建模进行了测试。为了提高应用模型的效率，使用了两种后处理技术，即小波变换（WT）和集成经验模式分解（EEMD）。此外，还考虑了基于电台自身数据（状态1）和先前电台数据（状态2）的两种建模状态。单个和集成的KELM模型结果比较表明，集成的WT和EEMD-KELM模型可产生更准确的结果。结果表明，WT的数据处理在提高模型效率方面比EEMD更有效。数据处理将模型的功能提高了15％。结果表明，状态1建模产生了更好的结果，但是，当无法使用站自身的数据时，使用集成的KELM方法可以将先前的站数据成功应用于SSC和SSD建模。

强调

• 通过连续水文站的人工智能方法预测了悬浮泥沙浓度（SSC）和悬浮泥沙排放量（SSD）。评估了数据预处理对模型效率提高的影响。敏感性分析表明，最有效的子系列是从预处理模型中获得的。