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Published: Građevinar 72 (2020) 3
Paper type: Scientific paper-Preliminary report
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Prediction of maximum annual flood discharges using artificial neural network approaches

Tugce Anilan, Sinan Nacar, Murat Kankal, Omer Yuksek

Abstract

The applicability of artificial neural network (ANN) approaches for estimation of maximum annual flows is investigated in the paper. The performance of three neural network models is compared: multi layer perceptron neural networks (MLP_NN), generalized feed forward neural networks (GFF_NN), and principal component analysis with neural networks (PCA_NN). The proposed approaches were applied to 33 stream-gauging stations. It was found that the optimal 3-hidden layered PCA_NN method was more appropriate than the optimal MLP_NN and GFF_NN models for the estimation of maximum annual flows.

Keywords
artificial neural networks, principal component analysis, maximum annual flows

HOW TO CITE THIS ARTICLE:

Anilan, T., Nacar, S., Kankal, M., Yuksek, O.: Prediction of maximum annual flood discharges using artificial neural network approaches, GRAĐEVINAR, 72 (2020) 3, doi: https://doi.org/10.14256/JCE.2316.2018

OR:

Anilan, T., Nacar, S., Kankal, M., Yuksek, O. (2020). Prediction of maximum annual flood discharges using artificial neural network approaches, GRAĐEVINAR, 72 (3), doi: https://doi.org/10.14256/JCE.2316.2018

LICENCE:

Creative Commons License
This paper is licensed under a Creative Commons Attribution 4.0 International License.
Authors:
Anlan WEB
Tugce Anilan
Karadeniz Technical University, Turkey
Faculty of Engineering, Dep. of Civil Engineering
Nacar WEB
Sinan Nacar
Karadeniz Technical University, Turkey
Faculty of Engineering, Dep. of Civil Engineering
Kankal WEB
Murat Kankal
Karadeniz Technical University, Turkey
Faculty of Engineering, Dep. of Civil Engineering
Yuksek WEB
Omer Yuksek
Karadeniz Technical University, Turkey
Faculty of Engineering, Dep. of Civil Engineering