Prediction of maximum annual flood discharges using artificial neural network approaches
Yazarlar (4)
Karadeniz Teknik Üniversitesi, Türkiye
Bursa Uludağ Üniversitesi, Türkiye
Karadeniz Teknik Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
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| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | GRADEVINAR |
| Dergi ISSN | 0350-2465 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q4 |
| Makale Dili | İngilizce |
| Basım Tarihi | 01-2020 |
| Cilt No | 72 |
| Sayı | 3 |
| Sayfalar | 215 / 224 |
| DOI Numarası | 10.14256/JCE.2316.2018 |
| Makale Linki | http://dx.doi.org/10.14256/jce.2316.2018 |
| Özet |
| 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. |
| Anahtar Kelimeler |
| artificial neural networks | principal component analysis | maximum annual flows |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 9 |
| Google Scholar | 10 |