Prediction of Parameters which Affect Beach Nourishment Performance Using MARS, TLBO, and Conventional Regression Techniques
Yazarlar (5)
Recep Tayyip Erdoğan Üniversitesi, Türkiye
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 | THALASSAS |
| Dergi ISSN | 0212-5919 Wos Dergi Scopus Dergi |
| Dergi Tarandığı Indeksler | SCI-Expanded |
| Dergi Grubu | Q4 |
| Makale Dili | İngilizce |
| Basım Tarihi | 04-2020 |
| Cilt No | 36 |
| Sayı | 1 |
| Sayfalar | 245 / 260 |
| DOI Numarası | 10.1007/s41208-019-00173-z |
| Makale Linki | http://link.springer.com/10.1007/s41208-019-00173-z |
| Özet |
| Artificial beach nourishment is one of the most important environmentally friendly coastal protection methods since it protects the aesthetic and recreational values of the beach and increases its protective properties. Therefore, the main aim of the current study is to assess the accuracy of multivariate adaptive regression splines (MARS) in predicting the parameters, namely sediment transport coefficients (K) and the diffusion rate (Ω), which affect beach nourishment performance. The performance of the MARS was determined by comparison of the models using exponential, linear, and power regression equations trained by conventional regression analyses (CRA) and the teaching-learning based optimization (TLBO) algorithm. In all models, two different input data obtained from the experimental study were used, one dimensional and one non-dimensional. The results presented that the MARS models gave lower … |
| Anahtar Kelimeler |
| Beach nourishment | Multivariate adaptive regression splines | Sediment transport | Shore protection | Teaching-learning based optimization |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| WoS | 6 |
| Google Scholar | 7 |