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

Acta Geophysica

article

Yilmaz, Banu¹  Aras, Egemen²  Kankal, Murat³  Nacar, Sinan⁴ 

[1] Department of Civil Engineering, Faculty of Technology, Karadeniz Technical University;Department of Civil Engineering, Faculty of Engineering and Natural Sciences, Bursa Technical University;Department of Civil Engineering, Faculty of Engineering, Bursa Uludağ University;Department of Civil Engineering, Faculty of Engineering, Karadeniz Technical University

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

DOI  :  10.1007/s11600-019-00374-3

学科分类：地球科学(综合)

来源: Polska Akademia Nauk * Instytut Geofizyki

【 摘 要 】

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.

【 授权许可】

Unknown