Please use this identifier to cite or link to this item: http://hdl.handle.net/11452/29757
Title: Prediction of maximum annual flood discharges using artificial neural network approaches
Authors: Anılan, Tuğçe
Nacar, Sinan
Yüksek, Ömer
Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.
0000-0003-0897-4742
Kankal, Murat
AAZ-6851-2020
24471611900
Keywords: Artificial neural networks
Principal component analysis
Maximum annual flows
L-moments approach
Frequency-analysis
Index-flood
Feedforward networks
Streamflow
Basin
Classification
Rainfall
Quality
Engineering
Issue Date: 10-Apr-2020
Publisher: Croatian Society of Civil Engineers
Citation: Anılan, T. vd. (2020). "Prediction of maximum annual flood discharges using artificial neural network approaches". Gradevinar, 72(3), 215-224.
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.
URI: https://doi.org/10.14256/JCE.2316.2018
http://www.casopis-gradjevinar.hr/archive/article/2316
http://hdl.handle.net/11452/29757
ISSN: 0350-2465
Appears in Collections:Scopus
Web of Science

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