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Prediction of cross-shore sandbar volumes using neural network approach

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Abstract

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.

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Author information

Authors and Affiliations

  1. Hydraulics Division, Civil Engineering Department, Engineering Faculty, Mustafa Kemal University, 31040, Iskenderun, Hatay, Turkey

    Mustafa Demirci & Fatih Üneş

  2. Hydraulics Division, Civil Engineering Department, Engineering and Architecture Faculty, Çukurova University, 01100, Adana, Turkey

    M. Sami Aköz

Authors
  1. Mustafa Demirci
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  2. Fatih Üneş
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  3. M. Sami Aköz
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Correspondence to Mustafa Demirci.

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Demirci, M., Üneş, F. & Aköz, M.S. Prediction of cross-shore sandbar volumes using neural network approach. J Mar Sci Technol 20, 171–179 (2015). https://doi.org/10.1007/s00773-014-0279-9

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  • DOI: https://doi.org/10.1007/s00773-014-0279-9

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