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

  • Banu Yilmaz
  • Egemen ArasEmail author
  • Murat Kankal
  • Sinan Nacar
Research Article - Hydrology


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.


Artificial bee colony Çoruh river basin Estimation Suspended sediment loading Teaching–learning-based optimization 



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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2019

Authors and Affiliations

  1. 1.Department of Civil Engineering, Faculty of TechnologyKaradeniz Technical UniversityTrabzonTurkey
  2. 2.Department of Civil Engineering, Faculty of Engineering and Natural SciencesBursa Technical UniversityBursaTurkey
  3. 3.Department of Civil Engineering, Faculty of EngineeringBursa Uludağ UniversityBursaTurkey
  4. 4.Department of Civil Engineering, Faculty of EngineeringKaradeniz Technical UniversityTrabzonTurkey

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