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Prediction of suspended sediment loading by means of hybrid artificial intelligence approaches

  • Research Article - Hydrology
  • Published:
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Abstract

The main aim of the research is to use the artificial neural network (ANN) model with the artificial bee colony (ABC) and teaching–learning-based optimization (TLBO) algorithms for estimating suspended sediment loading. The stream flow per month and SSL data obtained from two stations, İnanlı and Altınsu, in Çoruh River Basin of Turkey were taken as precedent. While stream flow and previous SSL were used as input parameters, only SSL data were used as output parameters for all models. The successes of the ANN-ABC and ANN-TLBO models that were developed in the research were contrasted with performance of conventional ANN model trained by BP (back-propagation). In addition to these algorithms, linear regression method was applied and compared with others. Root-mean-square and mean absolute error were used as success assessing criteria for model accuracy. When the overall situation is evaluated according to errors of the testing datasets, it was found that ANN-ABC and ANN-TLBO algorithms are more outstanding than conventional ANN model trained by BP.

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Acknowledgments

The authors thank YEGM (General Directorate of Renewable Energy) for the hydrological data of the research. This study is dedicated in memory of the late Assoc. Prof. Dr. Murat İhsan KÖMÜRCÜ, who died in February 2013.

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Technology, Karadeniz Technical University, 61830, Trabzon, Turkey

    Banu Yilmaz

  2. Department of Civil Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University, 16330, Bursa, Turkey

    Egemen Aras

  3. Department of Civil Engineering, Faculty of Engineering, Bursa Uludağ University, 16059, Bursa, Turkey

    Murat Kankal

  4. Department of Civil Engineering, Faculty of Engineering, Karadeniz Technical University, 61080, Trabzon, Turkey

    Sinan Nacar

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  1. Banu Yilmaz
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  2. Egemen Aras
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  3. Murat Kankal
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  4. Sinan Nacar
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Correspondence to Egemen Aras.

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Yilmaz, B., Aras, E., Kankal, M. et al. Prediction of suspended sediment loading by means of hybrid artificial intelligence approaches. Acta Geophys. 67, 1693–1705 (2019). https://doi.org/10.1007/s11600-019-00374-3

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  • DOI: https://doi.org/10.1007/s11600-019-00374-3

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