RBF network for spatial mapping of wave heights

Abstract Satellite-based information on significant waves is increasingly being made available to ocean scientists and engineers at low costs. While such measurements have a number of useful applications the manner with which the satellites sense the waves limits their applications to nearshore locations. This paper discusses an approach based on the radial basis function (RBF) type of artificial neural networks to map remote-sensed deep-water waves with the coastal waves. Significant wave heights at a number of locations over a track parallel to the coastline are used to estimate the significant wave heights at a nearshore site. The success of the method adopted was confirmed from the satisfactory error measures it produced during the testing carried out following the network training. It was also found that the satellite data need a ‘local tuning’ as done in the RBF before their further use in network computations. This work also highlighted importance of innovative approaches to calibrate a network on the basis of a given data set.

[1]  Sibel Pamukcu,et al.  Data generation for shear modulus and damping ratio in reinforced sands using adaptive neuro-fuzzy inference system , 2004 .

[2]  Joo-Hwa Tay,et al.  A fast predicting neural fuzzy model for high-rate anaerobic wastewater treatment systems , 2000 .

[3]  Vladimir M. Krasnopolsky,et al.  Neural network approximations for nonlinear interactions in wind wave spectra: direct mapping for wind seas in deep water , 2005 .

[4]  M. C. Deo,et al.  Forecasting wind with neural networks , 2003 .

[5]  Vladan Babovic,et al.  Neural networks as routine for error updating of numerical models , 2001 .

[6]  Hojjat Adeli,et al.  RADIAL BASIS FUNCTION NEURAL NETWORK FOR WORK ZONE CAPACITY AND QUEUE ESTIMATION , 2003 .

[7]  Shigeo Abe,et al.  Neural Networks and Fuzzy Systems , 1996, Springer US.

[8]  O. Makarynskyy,et al.  Improving wave predictions with artificial neural networks , 2004 .

[9]  Chang Lin,et al.  Neural network for wave forecasting among multi-stations , 2002 .

[10]  Akira Kawamura,et al.  Neural Networks for Rainfall Forecasting by Atmospheric Downscaling , 2004 .

[11]  Makarand Deo,et al.  Wave tranquility studies using neural networks , 2003 .

[12]  Vladimir M. Krasnopolsky,et al.  A neural network technique to improve computational efficiency of numerical oceanic models , 2002 .

[13]  Jian-Kang Wu,et al.  Neural networks and simulation methods , 1993 .

[14]  Dong-Sheng Jeng,et al.  Application of artificial neural networks in tide-forecasting , 2002 .

[15]  Mehmet Özger,et al.  Temporal significant wave height estimation from wind speed by perceptron Kalman filtering , 2004 .

[16]  M. C. Deo,et al.  Tide Prediction Using Neural Networks , 1998 .

[17]  Tarek Sayed,et al.  Comparison of Adaptive Network Based Fuzzy Inference Systems and B-spline Neuro-Fuzzy Mode Choice Models , 2003 .

[18]  Tsong-Lin Lee.  Back-propagation neural network for long-term tidal predictions , 2004 .

[19]  John P. Grubert.  Prediction of Estuarine Instabilities with Artificial Neural Networks , 1995 .

[20]  A. R. Kambekar,et al.  Estimation of pile group scour using neural networks , 2003 .

[21]  P. C. Nayak,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

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

[23]  M. Vaziri.  Predicting Caspian Sea Surface Water Level by ANN and ARIMA Models , 1997 .

[24]  Ozgur Kisi,et al.  River Flow Modeling Using Artificial Neural Networks , 2004 .

[25]  Makarand Deo,et al.  Analysis of Wave Directional Spreading Using Neural Networks , 2002 .

[26]  Philip D. Wasserman,et al.  Advanced methods in neural computing , 1993, VNR computer library.

[27]  Daniel T. Cox,et al.  Water Level Observations and Short-Term Predictions Including Meteorological Events for Entrance of Galveston Bay, Texas , 2002 .

[28]  Nachimuthu Karunanithi,et al.  Neural Networks for River Flow Prediction , 1994 .