Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Comparative study of ocean wave spectrum using ENVISAT SAR data and wave rider buoy data(2006) Pai, J.; Kumar, R.; Sarkar, A.; Hegde, A.V.; Dwarakish, G.S.A comparative study of ENVISAT ASAR data and corresponding wave rider buoy data has been attempted. An algorithm has been developed to retrieve Ocean Wave Spectrum from SAR data. The resulting spectrum is compared with the wave rider buoy measured wave spectrum. To compute the 2-D image spectrum from multi-look SAR data, various corrections to the original SAR data has been applied. Thereafter, Modulation Transfer Function has been computed and utilized to convert image spectrum to the Ocean Wave Spectrum. This final ocean wave height spectrum is used to estimate the ocean wave spectral parameters and has been compared with the in-situ measurements and model derived wave spectrum. An attempt has also been made to process the Single Look Complex (SLC) data to reduce the speckle noise in the SAR data using Fast Fourier Transform (FFT).Item 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.Item Influence of wave and structure parameters on tranmission characteristics of himmf pipe breakwater with five layers([publishername] World Scientific, 2011) Mane, V.; Rajappa, S.; Rao, S.; Hegde, A.V.The paper presents the part results of a series of physical model scale experiments conducted for the study of variation of transmission coefficient Kt due to the horizontally interlaced multi-layered moored floating pipe (HIMMFP) breakwater. The studies were conducted on physical breakwater models having five layers of PVC (Poly Vinyl Chloride) pipes with wave steepness, Hi/gT2 (Hi=incident wave height, g=acceleration due to gravity and T=wave period) varying from 0.063 to 0.849; relative width, W/L (W=width of breakwater and L=wavelength) varying from 0.400 to 2.650 and relative spacing, S/D=3 (S=horizontal centre to centre spacing of pipes and D=diameter of pipe). The transmitted wave heights were measured, and data gathered was analyzed by plotting non-dimensional graphs depicting the variation of Kt with Hi/gT2 for values of d/W (d=depth of water) varying from 0.082 to 0.276; and also variation of Kt with W/L for values of Hi/d varying from 0.060 to 0.400. © 2019, World Scientific. All rights reserved.Item Hybrid genetic algorithm tuned support vector machine regression for wave transmission prediction of horizontally interlaced multilayer moored fl oating pipe breakwater(2011) Patil, S.G.; Mandal, S.; Hegde, A.V.; Muruganandam, A.Support Vector Machine (SVM) works on structural risk minimization principle that has greater generalization ability and is superior to the empirical risk minimization principle as adopted in conventional neural network models. However, it is noticed that one particular model in isolation cannot capture all data patterns easily. In the present paper, a hybrid genetic algorithm tuned support vector machine regression (HGASVMR) model was developed to predict wave transmission of horizontally interlaced multilayer moored fl oating pipe breakwater (HIMMFPB). Furthermore, parameters of both linear and nonlinear SVM models are determined by Genetic Algorithm. HGASVMR model was trained on the dataset obtained from experimental wave transmission of HIMMFPB using regular wave fl ume at Marine Structure Laboratory, National Institute of Technology, Surathkal, India. The results are compared with artifi cial neural network (ANN) model in terms of Correlation Coeffi cient, Root Mean Square Error and Scatter Index. Performance of HGASVMR is found to be reliably superior.Item Conventional prediction vs beyond data range prediction of loss coefficient for quarter circle breakwater using ANFIS(Springer Verlag service@springer.de, 2015) Hegde, A.V.; Raju, B.Protecting the lagoon area from the wave attack is one of the primary challenges in coastal engineering. Due to the scarcity of rubble and also to achieve economy, new types of breakwaters are being used in place of conventional rubble mound breakwaters. Emerged Perforated Quarter Circle Breakwaters (EPQCB) are artificial concrete breakwaters consisting of a curved perforated face fronting the waves with a vertical wall on rear side and a base slab resting on a low rubble mound base. The perforated curved front face has advantages like energy dissipation and good stability with less material as it is hollow inside. The estimation of hydrodynamic performance characteristics of EPQCB by physical model studies is complex, expensive and time consuming. Hence, computational intelligence (CI) methods are adopted for the evaluation of the performance characteristics like reflection, dissipation, transmission, runup, rundown etc. A number of CI methods like Artificial Neural Network (ANN), Fuzzy logic, and hybrids such as ANFIS, ANN-PCO (particle swarm optimization), ANN-ACO etc., are available and are being used. The paper presents the work carried out to predict the dependent output variable of loss coefficient (Kl) beyond the range of values of one of the input variables i.e., wave period (T) adopted in present work, using the input data on variables of wave height (H), wave period (T), structure height (hs), water depth (d), radius of the breakwater (R), spacing of perforations (S) and diameter of perforations (D) using ANFIS. For this purpose, both the conventional method of data segregation and also a new method called ‘beyond data range’ method are used for both training the ANFIS models and also to predict the dependent variable. Further, the input data was fed to the models in both dimensional and nondimensional form in order to understand the effect of using non-dimensional data in place of dimensional parametric data. The performance of ANFIS models for all the four cases mentioned above was studied and it was found that prediction using conventional method with non-dimensional parameters performed better than other three methods. ANFIS models can be used to predict the performance characteristic Kl of EPQCB beyond the input data range of wave period T. © Springer International Publishing Switzerland 2015.Item Bathymetry mapping using landsat 8 satellite imagery(Elsevier Ltd, 2015) Pushparaj, P.; Akshaya, B.J.; Hegde, A.V.Bathymetry is the science of determining the topography of the seafloor. Bathymetry data is used to generate navigational charts, seafloor profile, biological oceanography, beach erosion, sea level rise, etc. A number of methods are available for determining ocean bathymetry, using either active sensor such as sonar, lidar or passive multispectral imagery such as Ikonos, WorldView and Landsat. Determining the bathymetry using sonar and LiDAR is very expensive, while Ikonos and Worldview are commercially available multispectral satellite platforms whereas Landsat satellite imagery provides a free and publicly available data. Therefore, the present study makes an attempt to determine the bathymetry mapping of the southwest coast of India (13° 0' 0" N and 74°50' 0" E) by applying the ratio transform algorithm on the blue and green bands of Landsat 8 satellite imagery. The statistical indices such as R2, RMSE and MAE are computed between the algorithm derived value and the hydrographic chart sounding value. The result shows a good correlation between the algorithm derived value and hydrographic chart sounding value. © 2015 The Authors. Published by Elsevier Ltd.Item Computational intelligence on hydrodynamic performance characteristics of Emerged Perforated Quarter Circle Breakwater(Elsevier Ltd, 2015) Raju, B.; Hegde, A.V.; Sekhar, O.Protecting the lagoon area from the wave attack is one of the primary challenges in coastal engineering. Due to the scarcity of rubble and to achieve economy, new types of breakwaters are being used in place of conventional rubble mound breakwaters. Emerged Perforated Quarter Circle Breakwater (EPQCB) is an artificial concrete breakwater consisting of a curved perforated face fronting the waves, a vertical wall on back and a base slab resting on a low rubble mound base. The perforated curved front face is having advantages like energy dissipation and good stability with less material as it is hollow inside. Computational Intelligence (CI) can be adopted for the evaluation of performance characteristics like reflection, dissipation, run-up and rundown which are complex, time consuming and expensive to perform in laboratory. The paper presents the work carried out to predict the reflection coefficient (Kr) for input parameters, wave period (T) beyond the data range used for training and of wave height (H) along with the data on input parameters of water depth (d), spacing-perforation ratio (S/D) and radius (R) of the EPQCB. The data on various parameters are taken in two categories for training and testing of ANN as mentioned below in order to understand the effect of using non-dimensional data in place of parametric values: 1) Input in the form of parametric data (H, T, d, R, S, D), and 2) Input in the form of non-dimensional values (H/gT2, d/gT2, S/D, R/H). Better correlation was found when individual dimensional parametric data was used instead of non-dimensional group values in both the methods of prediction. Similarly, the correlation between the beyond the data range prediction and actual values was found to be good in both methods of prediction. © 2015 The Authors. Published by Elsevier Ltd.