Faculty Publications
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Item Below the Data Range Prediction of Soft Computing Wave Reflection of Semicircular Breakwater(Harbin Engineering University, 2019) Kundapura, S.; Arkal, V.H.; Pinho, J.L.S.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. © 2019, Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature.Item PSO-ANFIS hybrid approach for prediction of wave reflection coefficient for semicircular breakwater(Taylor and Francis Ltd., 2021) Kundapura, S.; Hegde, A.V.Breakwaters are used to provide protection to the coast and are being improved over the years through research. Semicircular breakwater (SBW) is one such contribution in the area of coastal structures with an improved esthetics and stability. Advances in artificial intelligence applications in several fields have led to the increased interest in the researchers of coastal engineering to venture into it. This paper focuses on the prediction of reflection coefficient (Kr) for SBW using adaptive neuro-fuzzy inference system (ANFIS) and a hybrid of particle swarm optimization for adaptive neuro-fuzzy inference system (PSO-ANFIS) for a wide range of wave heights. The datasets required for the study are acquired from the experimental investigations of SBW in the regular wave flume at the Marine Structure Laboratory, National Institute of Technology Karnataka, India. The data fed for training and testing were taken in two forms separately, i.e. dimensional and dimensionless form. The PSO-ANFIS based optimized prediction of reflection coefficient is compared with the prediction arrived through ANFIS-based learning. The accuracy assessment of prediction was done by correlation coefficient, scatter index, Nash–Sutcliffe efficiency, bias, and root mean square error. The PSO-ANFIS hybrid model prediction improved the ANFIS prediction for the considered cases. © 2018 Indian Society for Hydraulics.