2. Thesis and Dissertations

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End date: 2017Subject: search.filters.subject.Department of Applied Mechanics and Hydraulics

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    Participatory Geomatics in Process based watershed development
    (National Institute of Technology Karnataka, Surathkal, 2013) P. G, Diwakar; Mayya, S. G.
    Watershed, being a hydrological unit, has its importance as a physical, biological and socio-economic entity for planning and management of natural resources. Optimal use of land and water resources in a sustainable manner results in long-term benefits to the society. Developmental activities in rural areas for resource conservation are recognized as one of the major challenges and also a complex problem to deal with. Watershed development has been in vogue for a long time and several developmental programmes have been implemented over time, but there is a need to review the conventional methods. Remote Sensing and Geographic Information System technologies are well established in these areas. Further, it is noted that community participation in the developmental process, along with monitoring and evaluation, plays a key role. Considering that about 70% of Indians live in rural areas and large proportion of these areas depend on rain fed agriculture, spread over different agro-climatic zones, it is found pertinent to explore participatory methods for natural resources management. Not much work is done on process based participatory watershed development with geomatics technology interventions. The present research focus is on developing such a model with appropriate integration of modern tools and technologies. The conventional model is analysed and an improved process based model is suggested. The proposed model is suitably improved with community role at every stage of development with an optimal blend of conventional and contemporary techniques. Participatory geomatics and information technology solutions, through innovative means, are considered for watershed development including monitoring and evalution. The proposed techniques are successfully tested through Karnataka Watershed Development programme, Karnataka State, India and the results are discussed. The outcome indicates many positive developments, that is, effective use of modern technology in planning and implementation which has resulted in improved agriculture productvity, reduced runoof, increased infiltration, self employment, improved livestock and milk yield, better socio-economic conditions and livelihood options. It is concluded that innovative means of implementing participatory watershed development have given rich dividends for natural resources development.
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    Study of Geomorphology and Dynamics of Shoreline Associated with Mulky-Pavanje Rivermouth, Dakshina Kannada Coast, Karnataka, India
    (National Institute of Technology Karnataka, Surathkal, 2013) Nagaraj, Gumageri; Dwarakish, G. S.
    The current thesis considered Mulky-Pavanje rivermouth and associated shoreline of about 12km length, lies between 13000'00''-13006'00'' North Latitude and 74044'00''- 74050'00'' East Longitude of Dakshina Kannda coast, Karnataka, India for short-term (<10 years), medium-term (10–60 years) and long-term (>60 years) shoreline changes. Beach survey, beach width, wave climate (height, period and direction) and wind parameters (speed and direction) and sediment sampling are gathered from nine locations (BS 1 – BS 9) to represent total 12 km shoreline, during the period from September 2009 to December 2011 for short-term change analysis. Short-term change analysis indicated that net accretion on the beaches towards the south of the rivermouth (BS 1–BS 5), whereas the north of the rivermouth experienced net erosion (BS 6–BS 9). For medium-term shoreline change analysis, rainfall and river discharges are obtained from Indian Meteorological Department for the periods 1985- 2011 and 1985-1998 respectively. The monsoonal storm directly induces rivermouth morphology to vary (BS 5–BS 6), adjacent beaches to suffer from erosion (BS1–BS 4 and BS 7–BS 9) and also leads drastic changes in wave climate and freshwater flow. During monsoon and post-monsoon periods, the rivers Mulky (North) and Pavanje (South) overflow, discharge sizeable quantities of sediments into the sea, whereas during the pre-monsoon periods, seawater enters into the rivermouth area leads sediment deposition and distribution on either side of the rivermouth. However, the discharge of the Mulky river is approximately two times more than that of Pavanje river. Because of the more flow in the Mulky river, which runs across the northern part of the rivermouth, the shoreline in the vicinity of rivermouth is predominantly shifting towards south. Additionally long-term shoreline change analyses are made through multidated satellite imageries and topomaps for the period 1912–2009. The long-term shoreline change analyses depicts that northern spit and rivermouth are shifting towards south during the period 1912–2009 and also observed that fluctuation of accretion and erosion pattern on southern side of the shoreline is highly significant as compared with northern side. The Mulky-Pavanje rivermouth being highly complex and dynamic, but it provides wide scope for developmental activities around it. Therefore Land use/Land cover changes are attempted by considering recentix decade, i.e 1998–2009 with the help of topographical map and remote sensing data. Land use/Land cover change analysis indicated that, because of development of urbanization and industrialization around the rivermouth, the built-up area has been drastically increased, while the other coastal related geological features such as beach vegetation, mangroves and river sand are drastically reduced during the period 1998– 2009. In addition, Artificial Neural Network (ANN) technique is used to model the very important parameters of the coastal engineering such as wave height and littoral drift, which cause coastal erosion in the study area. The developed NARX and FFBP models are evaluated using error statistics. In both cases the NARX model performed better than FFBP and proved that wave height and littoral drift are the direct responsible factors to cause erosion in the Mulky-Pavanje rivermouth and associated shoreline.
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    Groundwater Level Forecasting using Radial Basis Function and Generalized Regression Neural Networks
    (National Institute of Technology Karnataka, Surathkal, 2013) D, Sreenivasulu; Deka, Paresh Chandra
    Forecasting of groundwater levels is very much useful for efficient planning in integrated management of groundwater and surface water resources in a basin. Accurate and reliable groundwater level forecasting models can help ensuring the sustainable use of a watershed’s aquifer for both urban and rural water supply. The present work investigates the potential of two Neural networks, such as Radial Basis Function Neural Networks (RBFNN) and Generalized Regression Neural Networks (GRNN) in comparison to regular ANN models like Feed Forward Back Propagation (FFBP) and Non-Linear Regression Model (NARX) for modeling in Ground water level (GWL) forecasting in a coastal aquifer at western Ghats of India. Total 24 wells (both shallow and deep) located within the study area (microwatershed of Pavanje river basin) were selected covering around 40sqkm. Here, two different dataset such as weekly Time series GWL and Meteorological variables those recorded during the study period (2004-2011) were used in the analysis. Various performance indices such as Root Mean Squared Error (RMSE), Coefficient of Correlation (CC) and Coefficient of Efficiency (CE) were used as evaluation criteria to assess the performance of the developed models. At the first stage, the potential and applicability of RBF for forecasting groundwater level are investigated. Weekly time series groundwater level data upto four lagged data has been used as various input scenario where predicted output are one and two week leadtime GWL. The analysis has been carried out separately for three representative open wells. For all the three well stations, higher accuracy and consistent forecasting performance for RBF network model was obtained compared to FFBP network model. After confirming the suitability of RBF in GWL forecasting and with better accuracy over FFBP, the work has been extended further to consolidate the applicability of RBF in multistep leadtime forecasting upto six week ahead. In this study, six representative wells are covered for development of RBF models for six different input combinations using lagged time series data. Outputs are the predicted GWL upto six week. RBF models are developed for every well station and results are compared with Non linear regression model (NARX). It has been observed that for allGroundwater level Forecasting using Radial Basis Function and Generalized Regression Neural Networks, Ph.D Thesis, 2012, NITK, Surathkal, India viii the six well station, the higher and consistent forecasting performance by RBF network model in multi step week lead which consolidates the forecasting capability of RBF. The NARX model result shows poor performance. In the third stage, to examine the potential and applicability of GRNN in GWL forecasting, various GRNN models has been developed by considering the advantage of S-summation and D-summation layers for different input combinations using time series data. Weekly time series groundwater level data upto four lagged data has been used as inputs where predicted outputs are one week leadtime GWL. The analysis has been carried out separately for three representative open wells. GRNN models were developed for every well and best model results were compared with best RBF and FFBP with LM training algorithm models. The RBF and GRNN models are almost performed similarly in GWL forecasting with higher accuracy in all the representative well station. The poor performance of FFBP-LM model is also satisfactory but found inferior than both GRNN and RBF. After confirming the potential and applicability of GRNN and RBF in time series GWL forecasting with similar capability, the robustness, adaptability and flexibility characteristics of these two techniques are further investigated for suitability with cause and effect relationship. Here various meteorological parameters are used as causable variable and the GWL is used as output effect .Only GRNN models are developed in the present study as RBF was found with similar predicting performance in previous studies. Five various input combinations are used to obtain best results as one step leadtime output for three representative wells. In this case also, GRNN model is predicting groundwater level with higher accuracy and with satisfactory results. The GRNN model performance is compared to general ANN (FFBP) model and found outperforming FFBP performance. The result of the study indicates the potential and suitability of RBFNN and GRNN modeling in GWL forecasting for multistep leadtime data. The performance of RBFNN and GRNN were found almost equally good. Although accuracy of forecasted GWL generally decreases with the increase of leadtime, the GWL forecast were obtained within acceptable accuracy for both the models.
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    Effect Of Data Preprocessing On The Prediction Accuracy Of Artificial Neural Network Model in Hydrologic Time Series
    (National Institute of Technology Karnataka, Surathkal, 2013) Banhatti, Aniruddha Gopal; Deka, Paresh Chandra
    The accurate prediction of hydrological behavior in both urban and rural watershed can provide valuable information for the urban planning, land use, design of civil projects and water resources management. Hydrology system is influenced by many factors such as weather, land cover, infiltration, evapotranspiration, so it includes a good deal of stochastic dependent component, multi-time scale and highly non-linear characteristics. Hydrologic time series are often non-linear and non- stationary. In spite of high flexibility of Artificial Neural Network (ANN) in modeling hydrologic time series, sometimes signals are highly non-stationary and exhibit seasonal irregularity. In such situation, ANN may not be able to cope with non-stationary data if pre-processing of input and/or output data is not performed. Pre-processing data refers to analyzing and transforming input and output variables in order to detect trends, minimize noise, underline important relationship and flatten the variables distribution in a time series. These analyses and transformations help the model learn relevant patterns. Pre-processing techniques, which facilitate stabilization of the mean and variance, and seasonality removal, are often applied to remove non- stationary aspect in data used to build soft computing models. In this study, different data pre-processing techniques are presented to deal with irregularity components that exist in a hydrologic time series data of the Brahmaputra basin within India at the Pandu gauging station near Guwahati city and Pancharatna gauging station further 150km downstream of Pandu by using daily time unit and their properties are evaluated by performing one step ahead flow forecasting using ANN. Three different preprocessed datasets are used for the analysis. Various ANN models are generated by varying network internal architecture with different input scenarios. The model results were evaluated by using Root Mean Square Error (RMSE)and Mean Absolute Percentage Error (MAPE) and found that Logarithmic based pre-processing techniques provide better forecasting performance among various pre-processing techniques. The results indicate that detecting non-stationary aspect and selecting an appropriate preprocessing technique is highly beneficial in improving the prediction performance of ANN model.
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    Computational Intelligence in Prediction of Wave Transmission for Horizontally Interlaced Multi-layer Moored Floating Pipe Breakwater
    (National Institute of Technology Karnataka, Surathkal, 2013) Govind, Patil Sanjay; Hegde, Arkal Vittal; Mandal, Sukomal
    Energy dissipation process of Horizontally Interlaced Multi-layer Moored Floating Pipe Breakwater (HIMMFPB) depends on various factors like pipe interference effect, the spacing between the pipes and number of layers. As the effect of all these factors on transmission is not clearly understood, it will be extremely difficult to quantify them mathematically. Furthermore, it is a complex problem, and till now there has not been available a simple mathematical model to predict the wave transmission through HIMMFPB by considering all the boundary conditions, and hence one has to depend on physical model studies which are expensive and time consuming. Computational Intelligence (CI) techniques, such as, Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine Regression (SVMR), Genetic Programming (GP) and Genetic Algorithm (GA) have been efficaciously proposed as an efficient tool for modelling and predictions in coastal/ocean engineering problems. For developing CI models in prediction of wave transmission for HIMMFPB, data set were obtained from experimental wave transmission of HIMMFPB using regular wave flume at Marine Structure Laboratory, National Institute of Technology, Karnataka, Surathkal, Mangalore, India. These data sets are divided into two groups, one for training and other for testing. The input parameters that influence the wave transmission Kt  of floating breakwater, such as, relative spacing to pipesS D, relative breakwater widthW L, ratio of incident wave height to water depthHi d, incident wave steepness Hi L are considered in developing CI models for prediction of wave transmission past HIMMFPB. In the present work, five layer pipes with S / D of 2, 3, 4 and 5 are considered. The ANN model is developed for prediction of wave transmission for HIMMFPB. Two network models, ANN1 and ANN2 are constructed based on the parameters which influence the wave transmission of floating breakwater. The input parameters of ANN1 model areW / L , Hi / d and Hi / L . To study over a range of spacing of pipesiv S / D on Kt , an input parameter, S / D is added to form ANN2 model. Training and testing of the network models are carried out for different hidden nodes and epochs. It is observed that the correlation (above 90%) between predicted wave transmission values by the network models and measured values are in good agreement. Furthermore, to improve the result of prediction of wave transmission of HIMMFPB, recently developed technique such as SVMR is used. This technique 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. Support vector machines (SVMs) are based on statistical learning theory. The basic idea of support vector machines is to map the original data x into a feature space with high dimensionality through a non-linear mapping function and construct an optimal hyper-plane in new space. Six SVMR models are constructed using kernel functions. In order to study the performance of each kernel in predicting wave transmission of HIMMFPB, SVMR is trained by applying these kernel functions. Performance of SVMR is based on the best setting of SVMR and kernel parameters. Correlation Coefficient (CC) of SVMR (b-spline) model (CC Train = 0.9779 and CC Test = 0.9685) is considerably better than other SVMR models. However, it is noticed that ANN model in isolation cannot capture all data patterns easily. Adaptive neuro-fuzzy inference system (ANFIS) uses hybrid learning algorithm, which is more effective than the pure gradient decent approach used in ANN. ANFIS models are developed to predict wave transmission of HIMMFPB. The performance of the ANFIS models in the prediction of Kt is compared with the measured values using statistical measures, such as, CC, Root mean Square Error ( RMSE ) and Scatter Index ( SI ). All the ANFIS models have shown CCs higher than or equal to 0.9510, with RMSE less than or equal to 0.051074 and SI less than or equal to 0.102296. ANFIS5 model predictions are very realistic when compared with the measured values (CC Train = 0.9723, CC Test = 0.9635). It is also observed that an S D plays an important role to train ANFIS5 model to map an input-output relation. Furthermore influence of input parameters is assessed using Principalv Component Analysis (PCA). It is observed that Hi / L is the least influential parameter Based on the PCA study discarding the least influential parameters, ANFIS6 model is developed. It is observed that the ANFIS models yield higher CCs as compared to that of ANN models. To improve the performance of SVMR and better selection of SVMR and kernel parameters, hybrid genetic algorithm tuned support vector machine regression (GASVMR) model is developed to predict wave transmission through HIMMFPB. Furthermore, parameters of both linear and nonlinear SVM models are determined by GA. The results are compared with ANN, SVMR and ANFIS models in terms of CC, RMSE and SI . Performance of GA-SVMR is found to be reliably superior. CI models can be utilized to provide a fast and reliable solution in prediction of the wave transmission for HIMMFPB, thereby making GA-SVMR as an alternate approach to map the wave structure interactions of HIMMFPB.
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    Hybrid Wavelet Transform-Neural Network Approach for Short Term and Long Term Time Series Flow Forecasting
    (National Institute of Technology Karnataka, Surathkal, 2014) Dadu, Khandekar Sachin; Deka, Paresh Chandra
    Accurate modeling of runoff is useful in urban and environmental planning, flood and water resources management. In this research, a hybrid model has been developed for Brahmaputra River flow forecasting based on wavelet and artificial neural network (ANN) methods. In this current study, discrete wavelet transform was linked to ANN naming Wavelet Artificial Neural Network (WANN) for flow forecasting. Ten year daily flow data from January 1990 to December 1999 of Pandu and Pancharatna stations on Brahmaputra River, which carries heavy flood in monsoon season in the North-East region of India, were used in the study. The observed flow data were decomposed (up to 7 level) to multiresolution time series via discrete wavelet transform using Daubechies wavelets of order ranging from 1 (db1) to 5 (db5). Then multiresolution time series data were fed as input to ANN to get the forecasted discharge values. Daily data were used to forecast flow values for lead times 2, 3, 4, 7 and 14 day, weekly data were used to forecast flow values for lead times 1 week and 2 week, and monthly data were used to forecast flow values for lead time 1 month. The root mean square error (RMSE), determination coefficient (R2), mean absolute error (MAE), BIAS (B), and scatter index (SI) were adopted to evaluate the model‟s performance. It was found that for all lead times WANN model has given better and consistent results compared to conventional ANN model. It was mainly because of multiresolution time series used as inputs. Also it was found that, model efficiency increases with increase in wavelet order, giving best results for db5 mother wavelet for all lead times for both the stations. Also, there has been significant impact of decomposition level on WANN model efficiency as observed in the study.
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    Development of Contrast Enhancement Algorithms for Coastal Applications using Satellite Images
    (National Institute of Technology Karnataka, Surathkal, 2014) A, Raju.; Dwarakish, G. S.; Reddy, Venkat D.
    Remotely sensed satellite images are used in many earth science applications such as geosciences studies, astronomy, and geographical information systems. One of the most important quality factors in satellite images comes from its contrast. Contrast enhancement is frequently referred to as one of the most important issues in image processing. Contrast is created by the difference in luminance reflectance from two adjacent surfaces. Image enhancement is one of the most interesting and important phase in the domain of digital image processing. The main purpose of image enhancement is to bring out details that are hidden in image, or to increase the contrast in a low contrast image. The quality of the remote sensing image depends on the reflected electromagnetic radiation from earth surfaces features. Lack of consistent and similar amounts of energy reflected by different features, results a low contrast satellite image. Enhancement of contrast is desirable for satellite images to identify and extract features, where features are essential in studying earth applications. The present study is carried out with a view to develop contrast enhancement algorithms for coastal applications using satellite images. Histogram Equalization (HE) is an effective and well-known indirect contrast enhancement method, where histogram of the image is modified. Because of stretching the global distribution of the intensity, the information laid on the histogram of the image will be lost by over enhancement and introducing unwanted artefacts. To overcome these drawbacks several HE-based methods are introduced. With the comparative study of existing HE-based methods, the present study has developed contrast enhancement algorithms for coastal applications such as, automatic shoreline detection, suspended sediment transport and land use and land cover assessment for Mangalore Coast, West Coast of India, starting from Thalapady in the South and Mulky in the North.The study has developed an automatic shoreline detection algorithm using clipped histogram equalization and thresholding techniques. Clipped histogram equalization method highlighted the coastal objects and thresholding operation precisely separated the land and water regions. The smoothed shoreline is extracted using Robert’s edge detector. The study area is divided into Mulky-Pavanje rivermouth and NetravatiGurpur rivermouth areas. The shorelines of both the regions are extracted from Indian Remote Sensing Satellite (IRS P6) LISS-III (2005, 2007 and 2010) and IRS R2 LISSIII (2013) satellite images using developed automatic shoreline detection method. The delineated shorelines have been analyzed using Digital Shoreline Analysis System (DSAS), a GIS Software tool for estimation of shoreline change rates through two statistical techniques such as, End Point Rate (EPR) and Linear Regression Rate (LRR). To enhance IRS-P4 OCM Oceasat-2 satellite image for sediment movement direction, study developed Clipped Histogram Equalization and Principal Component Analysis (PCA) based algorithm. The movement of dispersed suspended sediment pattern of Mangalore Coast, West Coast of India is detected and mapped using qualitative analysis. The study is mainly focused on suspended sediment distributions at Netravati-Gurpur Rivermouth along Mangalore Coast. To improve the assessment of land use and land cover, study developed contrast enhancement algorithm using clipped histogram equalization and Principal Component Analysis (PCA). IRS-R2 LISS III 2013 satellite image is used for assess the developed algorithm. For assessment, the study area is divided into MulkyPavanje rivermouth area, New Mangalore Port Trust (NMPT) area and NetravatiGurpur rivermouth area. The IRS-R2 LISS III 2013 satellite image is classified using maximum likelihood supervised classification method by considering GPS values and Google Earth map as reference in selection of training samples during the classification. The developed contrast enhancement algorithm has increased the accuracy assessment of LULC classification to 85.42%, 89.66% and 86.93% for Mulky-Pavanje river mouth area, New Mangalore Port Trust (NMPT) area and Netravati-Gurpur river mouth area respectively.
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    Damage Level Prediction of NonReshaped Berm Breakwater using Soft Computing Techniques
    (National Institute of Technology Karnataka, Surathkal, 2014) N, Harish.; Rao, Subba; Mandal, Sukomal
    Tranquility condition inside the port and harbor has to be maintained for loading cargo and passengers. In order to maintain calm condition inside the port and harbor, breakwater has to be constructed to dissipate wave energy that is coming inside. The alignment of the breakwater must be carefully considered after examining the predominant direction of approach of waves and winds, degree of protection required, magnitude and direction of littoral drift and the possible effect of these breakwaters on the shoreline. In general these studies are invariably conducted in a physical model test where various alternatives are studied and the final selection will be based on performance consistent with cost. Considering the coastal boundary and depth variation, field analysis of wave structure interaction, determination of stability and damage level of berm breakwater structure is difficult. Mathematical modeling of these complex interactions is difficult while physical modeling will be costly and time consuming. Hence one has to depend on physical model studies which are expensive and time consuming. Soft computing techniques, such as, Artificial Neural Network (ANN), Support Vector Machine (SVM),Adaptive Neuro-Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) have been efficiently proposed as a powerful tool for modeling and predictions in coastal/ocean engineering problems. For developing soft computing models in prediction of damage level of non-reshaped berm breakwater, data set are obtained from experimental damage level of non-reshaped berm breakwater using regular wave flume at Marine Structure Laboratory, National Institute of Technology, Karnataka, Surathkal, Mangalore, India. These data sets are divided into two groups, one for training and the other for testing. The input parameters that influence the damage level (S) of nonreshaped berm breakwater, such as, relative wave steepness (H/L0), surf similarity (ζ), slope angle (cotα) relative berm position by water depth (hB/d), relative armour stone weight (W50/W50max), relative berm width (B/ L0) and relative berm location (hB/L0) are considered in developing soft computing models for prediction damage level. The ANN model is developed for the prediction of damage level of non-reshaped berm breakwater. Two network models, ANN1 and ANN2 are constructed based on the parameters which influence the damage level of non-reshaped berm breakwater. The seven input parameters that are initially considered for ANN1 model are (H/L0), (ζ), (cotii α), (hB/d), (W50/W50max), (B/ L0) and (hB/L0). The ANN1 model is studied with different algorithm namely, Scaled Conjugate Gradient (SCG), Gradient Descent with Adaptive learning (GDA) and Levenberg-Marquardt Algorithm (LMA) with five numbers of hidden layer nodes and a constant 300 epochs. LMA showed good performance than the other algorithms. Also, influence of input parameters is evaluated using Principal Component Analysis (PCA). From PCA study, it is observed that cotα is the least influencing parameter on damage level. Based on the PCA study, least influencing parameter is discarded and ANN2 model is developed with remaining six input parameters. Training and testing of the ANN2 network models are carried out with LMA for different hidden layer nodes and epochs. The ANN2 with LMA 6-5-1 with 300 epochs gave good results. It is observed that the correlation of about 88% between predicted and observed damage level values by the ANN2 network models and measured values are in good agreement Furthermore, to improve the result of prediction of damage level of non-reshaped berm breakwater, SVM model was developed. This technique 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. This model was developed based on statistical learning theory. The basic idea of SVM is to map the original data x into a feature space with high dimensionality through a nonlinear mapping function and construct an optimal hyper-plane in new space. SVM models were constructed using different kernel functions. In order to study the performance of each kernel in predicting damage level of non-reshaped berm breakwater, SVM is trained by applying these kernel functions. Performance of SVM is based on the best setting of SVM and kernel parameters. Correlation Coefficient (CC) of SVM (polynomial) model (CC Train = 0.908 and CC Test = 0.888) is considerably better than other SVM models. To avoid over-fitting or under-fitting of the SVM model due to the improper selection of SVM and kernel parameters and also the performance of SVM, hybrid particle swarm optimization tuned support vector machine regression (PSO-SVM) model is developed to predict damage level of non-reshaped berm breakwater. The performance of the PSOSVM models in the prediction of damage level is compared with the measured values using statistical measures, such as, CC, Root mean Square Error (RMSE) and Scatteriii Index (SI). PSO-SVM model with polynomial kernel function gives realistic prediction when compared with the observed values (CC Train = 0.932, CC Test = 0.921). It is observed that the PSO-SVM models yield higher CCs as compared to that of SVM models. However, it is noticed that ANN model in isolation cannot capture all data patterns easily. Adaptive Neuro-Fuzzy Inference System (ANFIS) uses hybrid learning algorithm, which is more effective than the pure gradient decent approach used in ANN. ANFIS models were developed with different membership namely Triangular-shaped built-in membership function (TRIMF), Trapezoidal-shaped built-in membership function (TRAPMF), Generalized bell-shaped built-in membership function (GBELLMF), and Gaussian curve built-in membership function (GAUSSMF) to predict damage level of non-reshaped berm breakwater. The performance of the ANFIS models in the prediction of damage level is compared with the measured values using statistical measures, such as, CC, RMSE and SI. ANFIS model with GAUSSMF gave realistic prediction when compared with the observed values (CC Train = 0.997, CC Test = 0.938). It is observed that the ANFIS models yield higher CCs as compared to that of ANN models. The different soft computing models namely, ANN, SVM, PSO-SVM and ANFIS results are compared in terms of CC, RMSE, SI and computational time. The hybrid models in both (ANFIS and PSO-SVM) cases showed better results compared to individual models (ANN and SVM). When the hybrid models are compared, ANFIS model gives higher CC and lower RMSE. But considering computational time, ANFIS has taken more time than PSO-SVM model. Hence PSO-SVM is computationally efficient as compared to ANFIS. ANFIS and PSO-SVM models perform better and similar to observed values. Hence, ANFIS or PSO-SVM can replace the ANN, SVM for damage level prediction of nonreshaped berm breakwater. ANFIS or PSO-SVM can be utilized to provide a fast and reliable solution in prediction of the damage level prediction of non-reshaped berm breakwater, thereby making ANFIS or PSO-SVM as an alternate approach to map the wave structure interactions of berm breakwater.
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    Parametric Studies on Stability of Reshaped Berm Breakwater with Concrete Cubes as Armor Unit
    (National Institute of Technology Karnataka, Surathkal, 2014) J, Prashanth.; Rao, Subba; Shirlal, Kiran G.
    The breakwater construction in deeper waters requires heavier armor units due to larger wave loads. Such large stones are uneconomical to quarry or transport or may not be available nearby. Another problem is uncertainty in the design conditions resulting in breakwater damage due to increased wave loads. The structural stability and economy in construction of breakwater are the need of hour. Under these circumstances, berm breakwaters can be a solution. For an economical solution, the quarry yield may be judiciously used and berm breakwater may be constructed with small size armor units. The present research work involves a detailed experimental study of influence of various sea states and structural parameters on the stability of statically stable reshaped berm breakwater made of concrete cubes as primary armor. Initially, a 0.70 m high of 1:30 scale model of conventional breakwater of 1V:1.5H slope and trapezoidal cross section is constructed on the flume bed with concrete cubes of weight 106 g as primary armor. This is designed for a non-breaking wave of height 0.10 m. This model is tested for armor stability with regular waves of heights 0.10 m to 0.16 m and periods 1.6 s to 2.6 s in water depths of 0.30 m, 0.35 m and 0.40 m. In the second phase, a 1V:1.5H sloped and 0.70 m high berm breakwater with varying size of concrete armor cubes, berm widths, thickness of primary layer is tested for stability with same test conditions. Based on the study of conventional and reshaped berm breakwater model the following conclusions are drawn. Damage level (S) was found increasing with the increase in stability number (Ns) in conventional breakwater. In conventional breakwater damages were in the range of 4.62 to 5.69 (intermediate), 9.75 to 11.46 (failure) and 9.46 to 10.22 (failure) in the depths of 0.3m, 0.35m and 0.4m respectively. Considering the complete iranges of Ho/gT2 and d/gT2, the maximum relative run-up Ru/Ho and relative run-down Rd/Ho were respectively 1.2 and 1.25. The stability of the berm breakwater is largely influenced by the storm duration. It was observed that relative berm position (hb/d) has a greater influence on berm recession than wave run-up and run-down. As relative berm position (hb/d) parameter increases from 1.00 to 1.50, the berm recession decreased by up to 77% while the wave run-up and run-down decreases by 7% and 14% respectively. The surface elevation of the water in front of the berm influences the recession and eroded area of the berm. Some of the available equations for berm recession, wave run-up over estimated the values for the considered conditions. The damage is reduced by about 47% in the present model when compared to stone armored berm breakwater. The wave runup and run-down are reduced by 34% and 49% compared to conventional cube armored breakwater respectively. The economic analysis showed that the cube armored berm breakwater is about 8% and 4% economical than the conventional cube armored breakwater and stone armored berm breakwater for the same design conditions. The design equations for berm recession, wave run-up and wave run-down are derived. Finally, it was found that 25% reduction in armor weight with 0.40 m berm width and 2 no. of primary armor layers is safe for the most of conditions considered during the study except for extreme waves of 0.16 m height and 1.6 s period. However, same breakwater with 3 armor layers was safe for the entire range of test conditions. In terms of safety as well as economy 25% reduction in armor weight with 0.40 m berm width and 2 no. of primary layer was cheaper compared to all other models studied.
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    Characterization of Soil Hydraulic Properties in the Pavanje River Basin, Karnataka, India
    (National Institute of Technology Karnataka, Surathkal, 2014) Shwetha; K, Varija.
    Knowledge of the soil hydraulic properties is very important to solve many soil and water management problems related to agriculture, ecology, and environmental issues. The primary objective of this study was to characterize soil hydraulic properties for the Pavanje river basin soils that lie in the coastal region of Karnataka, India. This research work was mainly focused to develop and validate point and parametric PTF models based on nonlinear regression technique using the different set of predictors such as particle size distribution, bulk density, porosity and organic matter content. Soil samples were collected and subjected to laboratory measurements to get the basic soil properties such as particle size distribution, bulk density, and organic matter content and hydraulic properties like soil water characteristics curve and saturated hydraulic conductivity. The point PTF models estimated retention points at -33, -100, -300, -500, -1000, and -1500 kPa matric potentials and parametric PTF models estimated van Genuchten and BrooksCorey water retention parameters. The present study also developed and validated pedotransfer functions for the estimation of saturated hydraulic conductivity. In addition to this, an empirical relationship has been derived to approximate the soil moisture retention curve from saturated hydraulic conductivity for the sampled soils. The uncertainty analysis was done for all the measured and estimated soil physical and hydraulic properties. Runoff was also predicted for the forested hillslope soils from Green and Ampt infiltration method using measured values of saturated hydraulic conductivity, residual water content, porosity and water content at field capacity values. Finally spatial variability of all physical properties and hydraulic properties were studied for both agricultural and forested hillslope soils. The study of hydraulic properties done in this work could be very helpful for any hydrological modeling for this particular area.