Faculty Publications

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Author: search.filters.author.Patil, S.G.Subject: search.filters.subject.Artificial Neural Network

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    Application of Neural Networks in coastal engineering - An overview
    (2008) Mandal, S.; Patil, S.G.; Manjunatha, Y.R.; Hegde, A.V.
    Artificial Neural Network (ANN) is being applied to solve a wide variety of coastal/ocean engineering problems. In practical terms ANNs are non-linear modeling tools and they can be used to model complex relationship between the input and output system. In addition, ANNs have a very high degree of freedom and are very simple to train the system for any number of input values, which makes the network attractive and reliable. ANNs are ideally suited to find many solutions like pattern reorganization, data classification, forecasting future events and time series analysis. This paper gives an overview of application of ANN in the field of coastal engineering.
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    Genetic algorithm based support vector machine regression in predicting wave transmission of horizontally interlaced multi-layer moored floating pipe breakwater
    (Elsevier Ltd, 2012) Patil, S.G.; Mandal, S.; Hegde, A.V.
    Planning and design of coastal protection works like floating pipe breakwater require information about the performance characteristics of the structure in reducing the wave energy. Several researchers have carried out analytical and numerical studies on floating breakwaters in the past but failed to give a simple mathematical model to predict the wave transmission through floating breakwaters by considering all the boundary conditions. Computational intelligence techniques, such as, Artificial Neural Networks (ANN), fuzzy logic, genetic programming and Support Vector Machine (SVM) are successfully used to solve complex problems. In the present paper, a hybrid Genetic Algorithm Tuned Support Vector Machine Regression (GA-SVMR) model is developed to predict wave transmission of horizontally interlaced multilayer moored floating pipe breakwater (HIMMFPB). Furthermore, optimal SVM and kernel parameters of GA-SVMR models are determined by genetic algorithm. The GA-SVMR model is trained on the data set obtained from experimental wave transmission of HIMMFPB using regular wave flume at Marine Structure Laboratory, National Institute of Technology, Karnataka, Surathkal, Mangalore, India. The results are compared with ANN and Adaptive Neuro-Fuzzy Inference System (ANFIS) models in terms of correlation coefficient, root mean square error and scatter index. Performance of GA-SVMR is found to be reliably superior. b-spline kernel function performs better than other kernel functions for the given set of data. © 2011 Elsevier Ltd. All rights reserved.