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

• Autorzy: Yilmaz Banu , Aras Egemen , Kankal Murat , Nacar Sinan

Identyfikatory

DOI

10.1007/s11600-019-00374-3

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

artificial bee colony; Çoruh river basin; estimation; suspended sediment loading; teaching–learning-based optimization

PL

sztuczna kolonia pszczół; dorzecze rzeki Çoruh; oszacowanie

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2019
• Tom: Vol. 67, no. 6
• Strony: 1693--1705
• Opis fizyczny: Bibliogr. 58 poz.

Twórcy

autor

Yilmaz Banu

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

autor

Aras Egemen

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

autor

Kankal Murat

• Department of Civil Engineering, Faculty of Engineering, Bursa Uludağ University, 16059 Bursa, Turkey

autor

Nacar Sinan

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

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).