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A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States

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An Erratum to this article was published on 24 April 2015

Abstract

Accurate and reliable suspended sediment load (SSL) prediction models are necessary for planning and management of water resource structures. More recently, soft computing techniques have been used in hydrological and environmental modeling. The present paper compared the accuracy of three different soft computing methods, namely, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), coupled wavelet and neural network (WANN), and conventional sediment rating curve (SRC) approaches for estimating the daily SSL in two gauging stations in the USA. The performances of these models were measured by the coefficient of correlation (R), Nash-Sutcliffe efficiency coefficient (CE), root-mean-square error (RMSE), and mean absolute percentage error (MAPE) to choose the best fit model. Obtained results demonstrated that applied soft computing models were in good agreement with the observed SSL values, while they depicted better results than the conventional SRC method. The comparison of estimation accuracies of various models illustrated that the WANN was the most accurate model in SSL estimation in comparison to other models. For example, in Flathead River station, the determination coefficient was 0.91 for the best WANN model, while it was 0.65, 0.75, and 0.481 for the best ANN, ANFIS, and SRC models, and also in the Santa Clara River, amounts of this statistical criteria was 0.92 for the best WANN model, while it was 0.76, 0.78, and 0.39 for the best ANN, ANFIS, and SRC models, respectively. Also, the values of cumulative suspended sediment load computed by the best WANN model were closer to the observed data than the other models. In general, results indicated that the WANN model could satisfactorily mimic phenomenon, acceptably estimate cumulative SSL, and reasonably predict peak SSL values.

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Acknowledgments

The authors would like to thank two anonymous reviewers for their critical review and comments on this manuscript.

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Authors and Affiliations

  1. Young Researchers and Elites Club, Hamedan Branch, Islamic Azad University, Hamedan, Iran

    Ehsan Olyaie

  2. Department of Water Engineering, College of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

    Hossein Banejad

  3. Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China

    Kwok-Wing Chau

  4. Department of Earth and Environment, Florida International University, Miami, FL, USA

    Assefa M. Melesse

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  1. Ehsan Olyaie
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Correspondence to Hossein Banejad.

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An erratum to this article is available at http://dx.doi.org/10.1007/s10661-015-4526-2.

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Olyaie, E., Banejad, H., Chau, KW. et al. A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States. Environ Monit Assess 187, 189 (2015). https://doi.org/10.1007/s10661-015-4381-1

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