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The objective of this study was to develop artificial intelligence\u2010 (AI\u2010) based ensemble methods for modeling SSL in Katar catchment, Ethiopia. In this paper, three single AI\u2010based models, that is, support vector machine (SVM), adaptive neurofuzzy inference system (ANFIS), feed\u2010forward neural network (FFNN), and one conventional multilinear regression (MLR) modes, were used for SSL modeling. Besides, four different ensemble methods, neural network ensemble (NNE), ANFIS ensemble (AE), weighted average ensemble (WAE), and simple average ensemble (SAE), were developed by combining the outputs of the four single models to improve their predictive performance. The study used two\u2010year (2016\u20102017) discharge and SSL data for training and verification of the applied models. Determination coefficient (DC) and root mean square error (RMSE) were used to evaluate the performances of the developed models. Based on the performance measure results, the ANFIS model provides higher efficiency than the other developed single models. Out of all developed ensemble models, the nonlinear ANFIS model combination method was found to be the most accurate method and could increase the efficiency of SVM, MLR, ANFIS, and FFNN models by 19.02%, 37%, 9.73%, and 16.3%, respectively, at the verification stage. Overall, the proposed ensemble models in general and the AI\u2010based ensemble in particular provide excellent performance in SSL estimation.<\/jats:p>","DOI":"10.1155\/2021\/6633760","type":"journal-article","created":{"date-parts":[[2021,2,17]],"date-time":"2021-02-17T18:37:01Z","timestamp":1613587021000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Estimation of Suspended Sediment Load Using Artificial Intelligence\u2010Based Ensemble 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