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Başlık: Prediction of parameters which affect beach nourishment performance using MARS, TLBO, and conventional regression techniques
Yazarlar: Karasu, Servet
Nacar, Sinan
Uzlu, Ergun
Yüksek, Ömer
Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği.
0000-0003-0897-4742
Kankal, Murat
AAZ-6851-2020
24471611900
Anahtar kelimeler: Beach nourishment
Multivariate adaptive regression splines
Sediment transport
Shore protection
Teaching-learning based optimization
Learning-based optimization
Splines
Models
Evolution
Climate
Rates
Area
Set
Marine & freshwater biology
Oceanography
Yayın Tarihi: 9-Ağu-2019
Yayıncı: Springer
Atıf: Karasu, S. vd. (2020). "Prediction of parameters which affect beach nourishment performance using MARS, TLBO, and conventional regression techniques". Thalassas, 36(1), 245-260.
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 (omega), 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 error values than the CRA and TLBO models according to the root mean square error, mean absolute error, and scattering index criteria. When the models were evaluated, it was revealed that dimensional and non-dimensional models gave approximate results. We proved that the dimensional and non-dimensional MARS models can be used to estimate the (K) and (omega) values.
URI: https://doi.org/10.1007/s41208-019-00173-z
https://link.springer.com/article/10.1007/s41208-019-00173-z
http://hdl.handle.net/11452/31133
ISSN: 0212-5919
2366-1674
Koleksiyonlarda Görünür:Scopus
Web of Science

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