Prediction of cross-shore sandbar volumes using neural network approach

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 experime...

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Bibliographic Details
Published in:Journal of marine science and technology 2015-03, Vol.20 (1), p.171-179
Main Authors: Demirci, Mustafa, Üneş, Fatih, Aköz, M. Sami
Format: Article
Language:English
Subjects:
Analysis
Automotive Engineering
Coasts
Engineering
Engineering Design
Engineering Fluid Dynamics
Estimates
Experimental data
Learning theory
Linear equations
Marine
Mathematical models
Mechanical Engineering
Neural networks
Offshore Engineering
Original Article
Regression
Sand bars
Sediment transport
Sediments
Slopes
Steepness
Water quality
Waterfront development
Wave height
Wave power
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Summary: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 ( H 0 / L 0 ) and period ( T ), the bed slope ( m ) and the sediment diameter ( d 50 ). 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.
ISSN:0948-4280
1437-8213
DOI:10.1007/s00773-014-0279-9