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- MakaleYalnız künye2013Scopus SJRQ1Prediction of offshore bar-shape parameters resulted by cross-shore sediment transport using neural network
Kömü rcü, Murat Ihsan; Kömür, Mehmet Aydin; Akpinar, Adem; Özölçer, Ismail Hakki; Yüksek, Ömer
In order to understand the features of coastal zone and to utilize the coastal areas, it is necessary to determine the sediment movement and the resulting transport. Waves, topographic features, and material properties are known as the most important factors affecting the sediment movement and coastal profiles. In this study, by taking into consideration of wave height and period (H0, T), bed slope (m) and sediment diameter (d50), cross-shore sediment movement was studied in a physical model and various bar-shape parameters of the resultant erosion type profile were determined. Using 80 experimental data which are obtained from physical model studies, a neural network (NN) has been calibrated to predict bar-shape parameters of beach profiles. A sensitivity analysis was firstly carried out to decide data of training and test sets. Four different models, in which the rates of their training and testing set data were 80% and 20%, 70% and 30%, 60% and 40%, 50% and 50% were constituted and their performances were compared. It was determined that the model, in which the rate of its training and testing set data was 80% and 20%, respectively, has the best results. Therefore, a total of 64 experimental data were used as training set and the remainders of the experimental data were used as a testing set for the model. The performance of the NN model was compared with the regression equations developed in a previous study and the equations cited in literature indicating better performance over the equations.
AtıfScopus16WoS14OpenAlex22
İncele - MakaleYalnız künye2012Scopus SJRQ4Artificial neural networks for estimation of temporal rate coefficient of equilibrium bar volume
Kankal, Murat; Kömürcü, Murat Ihsan; Yüksek, Ömer; Akpinar, Adem
Present study consists the growth of a bar profile caused by cross-shore sediment transport. This is especially on growth of bar volume (V) toward equilibrium bar volume (Veq). Three analysis methods being a power and linear regression analysis (PRA and LRA) and an Artificial Neural Network (ANN) analysis were performed to determine empirical temporal rate coefficient (α). Forty-two experimental data were used for training set and the rest of the experimental data were used for testing set in the ANN analysis. As the results of analyses, the smallest average relative and root mean square error (RMSE) computed for the ANN methods are 7.578% and 0.029, respectively. It has been obtained that the ANN analysis, which is used for determination of α coefficient, gives reasonable results. Finally, bar volumes were calculated by means of computed α values and compared with the results of experimental data.
AtıfScopus3WoS5İncele