Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time

Abstract 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.

[1]  G. Sahoo,et al.  Flow forecasting for a Hawaii stream using rating curves and neural networks , 2006 .

[2]  Yong Peng,et al.  The Research of Monthly Discharge Predictor-corrector Model Based on Wavelet Decomposition , 2008 .

[3]  Bang-Fuh Chen,et al.  Wavelet and artificial neural network analyses of tide forecasting and supplement of tides around Taiwan and South China Sea , 2007 .

[4]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[5]  Paulin Coulibaly,et al.  Wavelet analysis of variability in annual Canadian streamflows , 2004 .

[6]  Dina Makarynska,et al.  Artificial neural networks in wave predictions at the west coast of Portugal , 2005, Comput. Geosci..

[7]  Linda See,et al.  Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning , 2006 .

[8]  Vahid Nourani,et al.  A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling , 2009 .

[9]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[10]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

[11]  Mehmet Özger,et al.  Significant wave height forecasting using wavelet fuzzy logic approach , 2010 .

[12]  S. Mallat.  A wavelet tour of signal processing , 1998 .

[13]  Makarand Deo,et al.  Real time wave forecasting using neural networks , 1998 .

[14]  Ping Wang,et al.  Multiscale characteristics of the rainy season rainfall and interdecadal decaying of summer monsoon in North China , 2003 .

[15]  Paul S. Addison,et al.  Wavelet Transform Analysis of Open Channel Wake Flows , 2001 .

[16]  Marc B. Parlange,et al.  Intermittency, local isotropy, and non‐Gaussian statistics in atmospheric surface layer turbulence , 1994 .

[17]  Shu-Guang Li,et al.  A computationally practical approach for modeling complex mean flows in mildly heterogeneous media , 2004 .

[18]  Ozgur Kisi,et al.  Streamflow Forecasting Using Different Artificial Neural Network Algorithms , 2007 .

[19]  J. D. Agrawal,et al.  On-line wave prediction , 2002 .

[20]  Chien-Ming Chou,et al.  Application of wavelet‐based multi‐model Kalman filters to real‐time flood forecasting , 2004 .