Prediction of suspended sediment loading by means of hybrid artificial intelligence approaches
Yazarlar (4)
Karadeniz Teknik Üniversitesi, Türkiye
Bursa Teknik Üniversitesi, Türkiye
Bursa Uludağ Üniversitesi, Türkiye
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale |
| Dergi Adı | ACTA GEOPHYSICA |
| Dergi ISSN | 1895-6572 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Makale Dili | İngilizce |
| Basım Tarihi | 12-2019 |
| Cilt No | 67 |
| Sayı | 6 |
| Sayfalar | 1693 / 1705 |
| DOI Numarası | 10.1007/s11600-019-00374-3 |
| Makale Linki | http://link.springer.com/10.1007/s11600-019-00374-3 |
| Özet |
| 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, Inanli and Altinsu, in Coruh 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. |
| Anahtar Kelimeler |
| Artificial bee colony | Coruh river basin | Estimation | Suspended sediment loading | Teaching-learning-based optimization |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 13 |
| Google Scholar | 16 |