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dc.contributor.authorDeka, P.C.
dc.contributor.authorMandal, S.
dc.contributor.authorPrahlada, R.
dc.date.accessioned2017-09-27T13:03:55Z
dc.date.available2017-09-27T13:03:55Z
dc.date.issued2010
dc.identifier.citationProceedings of National Conference on Hydraulics, Water Resources, Coastal and Environmental Engineering- HYDRO 2010, December 16-18, 2010, Maharishi Markandeshwar Engineering College (MMEC), Mullana . 230-235.
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/5140
dc.description.abstractHybrid wavelet artificial neural network (WLNN) has been applied in the present study to forecast significant wave heights (Hs). Here Discrete Wavelet Transformation is used to preprocess the time series data (Hs) prior to Artificial Neural Network (ANN) modeling. The transformed output data are used as inputs to ANN models. Various decomposition levels have been tried for a db3 wavelet to obtain optimal results. It is found that the performance of hybrid WLNN is better than that of ANN when lead time increased considering various performance indices
dc.language.isoen
dc.publisherMaharishi Markandeshwar Engineering College
dc.rightsCopyright [2010]. All efforts have been made to respect the copyright to the best of our knowledge. Inadvertent omissions, if brought to our notice, stand for correction and withdrawal of document from this repository.
dc.titleMultiresolution wavelet-ANN model for significant wave height forecasting.
dc.typeConference Article


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