Faculty Publications
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Item Beyond the data range approach to soft compute the reflection coefficient for emerged perforated semicircular breakwater(Springer, 2019) Kundapura, S.; Hegde, A.V.; Wazerkar, A.V.Prediction of reflection coefficient (Kr) for emerged perforated semicircular breakwater (EPSBW) using artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) is carried out in the present paper. A new approach has been adopted in the present work using ANN and ANFIS models for the prediction of the reflection coefficient (Kr) for the wave periods beyond the range of the dataset used for training the network. The experimental data obtained for a scaled down EPSBW model from regular wave flume experiments at Marine Structure laboratory of National Institute of Technology Karnataka, Surathkal, Mangaluru, India was used. The ensemble was segregated such that certain higher ranges of wave periods were excluded in the training, and possibility of prediction was checked. The independent input parameters (Hi, T, S, D, R, d, hs) that influence the reflection coefficient (Kr) are considered for training as well as testing, where Hi is the incident wave height, T is the wave period, S is the spacing of perforations, D is the diameter of the perforations, R is the radius of the breakwater, d is the depth of the water and hs is the structure height. The accuracy of predictions of reflection coefficient (Kr) is done based on the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). The study shows that ANN and ANFIS models may be used for prediction of reflection coefficient Kr of semicircular breakwater for beyond the data range of wave periods used for training. However, ANFIS outperformed ANN model in the prediction of Kr in the case of beyond the data range segregation method. © Springer Nature Singapore Pte Ltd. 2019.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 Multistep ahead groundwater level time-series forecasting using gaussian process regression and ANFIS(Springer Verlag service@springer.de, 2016) Naganna, N.S.; Deka, P.C.Groundwater level is regarded as an environmental indicator to quantify groundwater resources and their exploitation. In general, groundwater systems are characterized by complex and nonlinear features. Gaussian Process Regression (GPR) approach is employed in the present study to investigate its applicability in probabilistic forecasting of monthly groundwater level fluctuations at two shallow unconfined aquifers located in the Kumaradhara river basin near Sullia Taluk, India. A series of monthly groundwater level observations monitored during the period 2000–2013 is utilized for the simulation. Univariate time-series GPR and Adaptive Neuro Fuzzy Inference System (ANFIS) models are simulated and applied for multistep lead time forecasting of groundwater levels. Individual performance of the GPR and ANFIS models are comparatively evaluated using various statistical indices. In overall, simulation results reveal that GPR model provided reasonably accurate predictions than that of ANFIS during both training and testing phases. Thus, an effective GPR model is found to generate more precise probabilistic forecasts of groundwater levels. © Springer India 2016.Item Relative wave run-up parameter prediction of emerged semicircular breakwater(Springer Science and Business Media Deutschland GmbH, 2021) Kundapura, S.; Rao, S.; Arkal, V.H.Relative wave run-up parameter (Ru/Hi) on breakwaters is a vital component in fixing the elevation of the breakwater crest. In the present study, several soft computing methods has been employed to predict the wave run-up on the emerged seaside perforated semicircular breakwater for the prevailing Arabian sea wave climate, off Mangaluru coast in India. Unlike the mathematical modeling techniques, the soft computing tools have no complexity involved about understanding the nature of underlying process and prediction consumes less time when proper physical model data is available. The soft computing methods like artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), genetic algorithm based adaptive neuro fuzzy inference system (GA-ANFIS) and particle swarm based adaptive neuro fuzzy inference system (PSO-ANFIS) are the four models employed in the study. The ANN predicted well for the set architecture of (5-7-1). The ANFIS is used to predict the wave run-up on semicircular breakwater models using the hybrid efficiency of fuzzy logic and neural network. An initial FIS is generated for input variables by mapping the input-output data; the training is done using ANN; and the objective of GA and PSO is set to find the best FIS, reducing the root mean square error in the prediction of wave run-up. The most influencing input parameters (Hi/gT2, d/gT2, S/D, hs/d, R/Hi) are taken in non-dimensional form. The data required has been acquired from the physical model experiments conducted in the Marine structures laboratory of National Institute of Technology Karnataka (NITK), Surathkal, India. The GA-ANFIS prediction of wave run-up is found to be better than that of ANFIS prediction in terms of Correlation coefficient (R), Root mean square error (RMSE), Nash sutcliffe efficiency (NSE), Bias and Scatter index (SI). However, among the four models developed the ANN prediction outperformed the other three considered models with a higher R = 0.9467. © Springer Nature Singapore Pte Ltd 2021.Item Adaptive Neuro-Fuzzy Systems and Ensemble Methods in Joint Shear Prediction and Sensitivity Analysis(Springer Science and Business Media Deutschland GmbH, 2024) Palkar, S.S.; Palanisamy, T.In the absence of ductile design, beam-column joints form weak links in the frame during seismic activities, hence jeopardizing the entire structure. Deducing from the views of researchers, estimation of joint shear strength of RC beam-column joint is a necessity with a complexity. This complexity highlights the importance of machine learning models due to their data handling and predictive capabilities. This study used 233 beam-column joints with 132 exterior and 101 interior joints for training and testing the ensemble machine-learning models and an Adaptive neuro-fuzzy inference system. The performance indices of the models built and their comparison is carried out to find the optimum model to be deployed. The sensitivity analysis of the features considered was conducted to infer the differences in exterior and interior beam-column joints’ behavior. © 2023, Springer Science and Business Media Deutschland GmbH. All rights reserved.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.Item 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.Item Performance evaluation of ANFIS and SVM model in prediction of wave transmission over submerged reef of tandem breakwater(CESER Publications Post Box No. 113 Roorkee 247667, 2017) Kuntoji, G.S.; Rao, S.; Rao, M.; Mandal, S.Tandem breakwater plays a unique role in protecting the ports. It is an innovative breakwater concept consisting of conventional breakwater and a submerged reef operating in tandem. As the depth-limiting behaviour of reef, the tandem possesses less design risk for extreme events. For a tandem breakwater, the transmitted wave over the submerged reef plays avital role in the safety of the emergent breakwater. Coastal structures like breakwaters are massive in terms of size as well as in the costs. Any structure before finally being constructed has to be subjected to model investigations for its safety against the design parameters. The soft computing techniques such as ANFIS (Adaptive Neuro Fuzzy Inference system) and SVM (Support Vector Machine)models are developed using experimental data points to predict the hydraulic performance of submerged reef of tandem breakwater. The performances of two models are validated with measured data, with the help of statistical measures namelyRMSE (Root MeanSquare-Error), CC (CorrelationCo-efficient), SI (Scatter-Index) andNSE (Nash-Sutcliff Efficiency). The results testify that SVM model performed better with 0.965 CC, 0.0557 RMSE, 0.9113 NSE and 0.1503 SI compared to ANFIS model with 0.935 CC, 0.0754 RMSE, 0.869 NSE and 0.00233 SI. © 2017 by International Journal of Ecology & Development.Item Performance evaluation of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating evapotranspiration in arid regions of India(Springer London, 2017) Patil, A.P.; Deka, P.C.This paper evaluates the ability of wavelet transform in improving the accuracy of artificial neural network (ANN) and adaptive neuro-fuzzy interface systems (ANFIS) models. In this study, the performance of hybrid Wavelet-ANN and Wavelet-ANFIS models for estimating daily evapotranspiration in arid regions was evaluated. Prior to the development of models, gamma test was used to identify the best input combinations that could be used under limited data scenario. Performance of the proposed hybrid models was compared to ANN, ANFIS, and conventionally used Hargreaves equation. The results revealed that use of wavelet transform as data preprocessing technique enhanced the efficiency of ANN and ANFIS models. Wavelet-ANN and Wavelet-ANFIS performed reasonably better than other models. Better handling of wavelet-decomposed input variables enabled Wavelet-ANN models to perform slightly better than the Wavelet-ANFIS models. W-ANN2 (RMSE = 0.632 mm/day and R = 0.96) was found to be the best model for estimating daily evapotranspiration in arid regions. The proposed W-ANN2 model used second-level db3 wavelet-decomposed subseries of temperature and previous day evapotranspiration values as inputs. The study concludes that hybrid Wavelet-ANN and Wavelet-ANFIS models can be effectively used for modeling evapotranspiration. © 2015, The Natural Computing Applications Forum.Item Artificial intelligence approaches for spatial modeling of streambed hydraulic conductivity(Springer International Publishing, 2019) Naganna, S.R.; Deka, P.C.Saturated hydraulic conductivity (Ks) describes the water movement through saturated porous media. The hydraulic conductivity of streambed varies spatially owing to the variations in sediment distribution profiles all along the course of the stream. The artificial intelligence (AI) based spatial modeling schemes were instituted and tested to predict the spatial patterns of streambed hydraulic conductivity. The geographical coordinates (i.e., latitude and longitude) of the sampled locations from where the in situ hydraulic conductivity measurements were determined were used as model inputs to predict streambed Ks over spatial scale using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) paradigms. The statistical measures computed by using the actual versus predicted streambed Ks values of individual models were comparatively evaluated. The AI-based spatial models provided superior spatial Ks prediction efficiencies with respect to both the strategies/schemes considered. The model efficiencies of spatial modeling scheme 1 (i.e., Strategy 1) were better compared to Strategy 2 due to the incorporation of more number of sampling points for model training. For instance, the SVM model with NSE = 0.941 (Strategy 1) and NSE = 0.895 (Strategy 2) were the best among all the models for 2016 data. Based on the scatter plots and Taylor diagrams plotted, the SVM model predictions were found to be much efficient even though, the ANFIS predictions were less biased. Although ANN and ANFIS models provided a satisfactory level of predictions, the SVM model provided virtuous streambed Ks patterns owing to its inherent capability to adapt to input data that are non-monotone and nonlinearly separable. The tuning of SVM parameters via 3D grid search was responsible for higher efficiencies of SVM models. © 2019, Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.