Prediction of cross-shore sandbar volumes using neural network approach

被引：0
|
作者：
Mustafa Demirci
Fatih Üneş
M. Sami Aköz
机构：
[1] Mustafa Kemal University,Hydraulics Division, Civil Engineering Department, Engineering Faculty
[2] Çukurova University,Hydraulics Division, Civil Engineering Department, Engineering and Architecture Faculty
来源：
Journal of Marine Science and Technology | 2015年 / 20卷
关键词：
Artificial neural networks (ANN); Multi-linear regression (MLR); Bar volumes; Cross-shore sediment transport; Coastal dynamics;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Correct estimation of bar volumes, wave height, wave period and median sediment diameter is crucial for the designing of coastal structures and water quality problem. In this study, bar volumes caused by cross-shore sediment transport were investigated using a physical model and obtained 64 experimental data considering the wave steepness (H0/L0) and period (T), the bed slope (m) and the sediment diameter (d50). Artificial neural network (ANN) and multi-linear regression (MLR) are used for predicting the bar volumes. A multi layer perceptron is used as the ANN structure. The results show that the ANN model estimates are much closer to the experimental data than the MLR model estimates.
引用
收藏
页码：171 / 179
页数：8
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