Predicting temporal rate coefficient of bar volume using hybrid artificial intelligence approaches
Yazarlar (4)
Bursa Uludağ Üniversitesi, Türkiye
Karadeniz Teknik Üniversitesi, Türkiye
Karadeniz Teknik Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | JOURNAL OF MARINE SCIENCE AND TECHNOLOGY |
| Dergi ISSN | 0948-4280 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q2 |
| Makale Dili | İngilizce |
| Basım Tarihi | 09-2018 |
| Cilt No | 23 |
| Sayı | 3 |
| Sayfalar | 596 / 604 |
| DOI Numarası | 10.1007/s00773-017-0495-1 |
| Özet |
| To project the structures to be built in the coastal zone and to make the best use of the coastal area, the mechanism of sediment transport, including both longshore and cross-shore transport, in this region should be well known. Within this context, temporal change rate of cross-shore sediment transport is of vital importance, especially to predict the erosion quantitatively. In this study, hybrid artificial intelligence models based on physical model data were established to determine the α coefficient used to describe the temporal change of cross-shore sediment transport. Teaching–learning-based optimization (TLBO) and artificial bee colony (ABC) algorithms were used for training of artificial neural network (ANN) in the model setup. Then, these models were compared with the classical back propagation ANN (ANN-BP) model. Wave height and period, bed slope and sediment diameter were considered as input … |
| Anahtar Kelimeler |
| Bar volume | Neural networks | Sediment transport | Teaching-learning-based optimization | Artificial bee colony |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 7 |
| Google Scholar | 13 |