Faculty Publications
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Item Hybrid wavelet neural network model for improving forecasting accuracy of time series significant wave height(2011) Prahlada, R.; Deka, P.C.Forecasting of a time series ocean wave data for various lead times has been attempted using hybrid wavelet-Artificial neural networks (WLNN) approach in this study. To improve the model performance a wavelet transformation is attached prior to a predictor (ANN) and then analysis has been carried out. Here the wavelet transformation is used to decompose the original significant wave height (Hs) data into its sub signals in the form of approximation coefficients and detail coefficients. Further, these coefficients were fed to ANN as inputs and targets and the results obtained from the hybrid model are then reconstructed to obtain the predicted significant wave heights. The predicted results from the proposed model were compared with the single ANN results. From the results, it is concluded that the proposed model is working efficiently for predicting time series data, and also the error observed at the higher lead time was very less as compared to the single ANN. The effect of decomposition level is also analysed in thisstudy and their influence was observed significantly in the higher lead time forecasting. © 2011 CAFET-INNOVA TECHNICAL SOCIETY.Item Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time(2012) Deka, P.C.; Prahlada, R.Recently Artificial Neural network (ANN) was extensively used as non-linear inter-extrapolator for ocean wave forecasting as well as other application in ocean engineering. In this current study, the Wavelet transform was hybridised with ANN naming Wavelet Neural Network (WLNN) for significant wave height forecasting near Mangalore, west coast of India, upto 48 h lead time. The main time series of significant wave height data were decomposed to multiresolution time series using discrete wavelet transformations. Then, the multiresolution time series data were used as input of the ANN to forecast the significant wave height at different multistep lead time. It was shown how the proposed model, WLNN, that makes use of multiresolution time series as input, allows for more accurate and consistent predictions with respect to classical ANN models. The proposed wavelet model (WLNN) results revealed that it was better forecasted and consistent than single ANN model because of using multiresolution time series data as inputs. © 2012 Elsevier Ltd. All rights reserved.