Faculty Publications
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Item WAVE FORECASTING FOR THE WEST COAST OF IHDIA(American Society of Civil Engineers (ASCE), 1970) Dattatri, J.; Renukaradhya, P.S.The applicability of the general Wave Forecasting procedures like the SMB and the PNJ methods, to the Indian coasts is studied. The study consisted in analysing the bynoptic charts to obtain the necessary wind characteristics. The computed wind characteristics were used in the above Forecasting methods to yield significant wave heights These were compared with the wave characteristics as recorded by a sub-surface pressure type recorder after suitable modifications to account for the attenuation of wave pressure with depth. The predicted wave heights compare well with the recorded wave heights and the SMB method predicts wave heights better for the case studied. © 1970 American Society of Civil Engineers.Item Neuro-fuzzy based approach for wave transmission prediction of horizontally interlaced multilayer moored floating pipe breakwater(2011) Patil, S.G.; Mandal, S.; Hegde, A.V.; Alavandar, S.The ocean wave system in nature is very complicated and physical model studies on floating breakwaters are expensive and time consuming. Till now, there has not been available a simple mathematical model to predict the wave transmission through floating breakwaters by considering all the boundary conditions. This is due to complexity and vagueness associated with many of the governing variables and their effects on the performance of breakwater. In the present paper, Adaptive Neuro-Fuzzy Inference System (ANFIS), an implementation of a representative fuzzy inference system using a back-propagation neural network-like structure, with limited mathematical representation of the system, is developed. An ANFIS is trained on the data set obtained from experimental wave transmission of horizontally interlaced multilayer moored floating pipe breakwater using regular wave flume at Marine Structure Laboratory, National Institute of Technology Karnataka, Surathkal, India. Computer simulations conducted on this data shows the effectiveness of the approach in terms of statistical measures, such as correlation coefficient, root-mean-square error and scatter index. Influence of input parameters is assessed using the principal component analysis. Also results of ANFIS models are compared with that of artificial neural network models. © 2010 Elsevier Ltd. All rights reserved.
