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Citation:
　Suman Kundapura,Vittal Hegde Arkal,Jose L. S. Pinho.Below the Data Range Prediction of Soft Computing Wave Reflection of Semicircular Breakwater[J].Journal of Marine Science and Application,2019,(2):167-175.[doi:10.1007/s11804-019-00088-4]
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Below the Data Range Prediction of Soft Computing Wave Reflection of Semicircular Breakwater

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Title:
Below the Data Range Prediction of Soft Computing Wave Reflection of Semicircular Breakwater
Author(s):
Suman Kundapura1;  Vittal Hegde Arkal1;  Jose L. S. Pinho2
Affilations:
Author(s):
Suman Kundapura1;  Vittal Hegde Arkal1;  Jose L. S. Pinho2
1 Department of Applied Mechanics and Hydraulics, National Institute of Technology Karnataka, Surathkal, Mangaluru 575025, India;
2 Department of Civil Engineering, University of Minho, 4710-057 Braga, Portugal
Keywords:
Semicircular breakwater; Wave reflection; Below the data range; Artificial neural network; Adaptive neuro-fuzzy inference system
分类号:
-
DOI:
10.1007/s11804-019-00088-4
Abstract:
Coastal defenses such as the breakwaters are important structures to maintain the navigation conditions in a harbor. The estimation of their hydrodynamic characteristics is conventionally done using physical models, subjecting to higher costs and prolonged procedures. Soft computing methods prove to be useful tools, in cases where the data availability from physical models is limited. The present paper employs adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) models to the data obtained from physical model studies to develop a novel methodology to predict the reflection coefficient (Kr) of seaside perforated semicircular breakwaters under low wave heights, for which no physical model data is available. The prediction was done using the input parameters viz., incident wave height (Hi), wave period (T), center-to-center spacing of perforations (S), diameter of perforations (D), radius of semicircular caisson (R), water depth (d), and semicircular breakwater structure height (hs). The study shows the prediction below the available data range of wave heights is possible by ANFIS and ANN models. However, the ANFIS performed better with R2=0.9775 and the error reduced in comparison with the ANN model with R2=0.9751. Study includes conventional data segregation and prediction using ANN and ANFIS.

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Memo

Memo:
Received date:2017-7-3;Accepted date:2018-11-18。
Corresponding author:Suman Kundapura
Last Update: 2019-07-06