Ocean wave forecasting in the Gulf of Thailand during typhoon Linda 1997: Hard and soft computing approaches

References

• ASCE Task Committee on Application of Artificial Neural Networks in Hydrology . 2000a . Artificial neural networks in hydrology I: Preliminary concepts . J. Hydrologic Engrg., ASCE , 5 ( 2 ) : 115 – 123 .
   Web of Science ®Google Scholar
• ASCE Task Committee on Application of Artificial Neural Networks in Hydrology . 2000b . Artificial neural networks in hydrology II: Hydrologic applications . J. Hydrologic Engrg., ASCE , 5 ( 2 ) : 124 – 137 .
   Google Scholar
• Campolo , M , Andreussi , P and Soldati , A . 1999 . River flood forecasting with a neural network model . Water Resources Research , 35 : 1191 – 1197 .
   Web of Science ®Google Scholar
• Deo , MC and Naidu C , Srihar . 1999 . Real time wave forecasting using neural networks . Journal of Ocean Engineering , 26 : 191 – 203 .
   Web of Science ®Google Scholar
• Flood , I and Kartam , N . 1994 . Neural networks in civil engineering–I: Principles and understanding . Journal of Computing in Civil Engrg., ASCE , 8 ( 2 ) : 131 – 148 .
   Web of Science ®Google Scholar
• French , MN , Krajewski , WF and Cuykendall , RR . 1992 . Rainfall forecasting in space and time using a neural network . J. Hydro. , 137 : 1 – 31 .
   Web of Science ®Google Scholar
• Gunther , H , Hasselmann , S and Janssen , PAEM . Wamodel cycle 4 , DKRZ Report No. 4 . 1991, Hamburg
   Google Scholar
• Hasselmann , K . 1974 . On the characterization of ocean waves due to white capping . Boundary-Layer Meteorology , 6 : 107 – 127 .
   Google Scholar
• Hasselmann , S and Hasselmann , K . 1985 . Computations and parameterizations of the nonlinear energy transfer in a gravity wave spectrum. Part I: A new method for efficient computations of the exact nonlinear transfer integral . Journal of Physical Oceanography , 15 ( 11 ) : 1369 – 1377 .
   Web of Science ®Google Scholar
• Hasselmann , S , Hasselmann , K , Allender , JH and Barnett , TP . 1985 . Computations and parameterizations of the nonlinear energy transfer in a gravity wave spectrum. Part II: Parameterizations of the nonlinear energy transfer for application in wave models . Journal of Physical Oceanography , 15 ( 11 ) : 1378 – 1391 .
   Web of Science ®Google Scholar
• Hastie , T , Tibshirani , R and Friedman , J . 2001 . The Elements of Statistical Learning: Data Mining, Inference, and Prediction , Springer-Verlag .
   Google Scholar
• Hsieh , BB and Pratt , TC . Field data recovery in tidal system using artificial neural networks(ANNs), Coastal and Hydraulic Engineering Technical note CHETN-IV-38, U.S , Army Engineer Research and Development Center, Vicksburg, MS, 2001
   Google Scholar
• Hsu , K , Gupta , HV and Sorooshian , S . 1995 . Artificial neural networks modeling of the rainfall-runoff process . Water Resources Research , 31 ( 10 ) : 2517 – 2530 .
   Web of Science ®Google Scholar
• Hu , TS , Lam , KC and Ng , ST . 2001 . River flow time series prediction with a range-dependent neural network . Hydrol. Sci. J. , 46 ( 5 ) : 729 – 745 .
   Web of Science ®Google Scholar
• Janssen , PAEM . 1989 . Wave-induced stress and the drag of air flow over sea waves . Journal of Physical Oceanography. , 19 : 745 – 754 .
   Web of Science ®Google Scholar
• Janssen , PAEM . 1991 . Quasi-Linear theory of wind wave generation applied to wave forecasting . Journal of Physical Oceanography , 21 : 1631 – 1642 .
   Web of Science ®Google Scholar
• Komen , GJ , Cavaleri , L , Donelan , M , Hasselmann , K , Hasselmann , S and Janssen , PAEM . 1994 . Dynamics and Modelling of Ocean Waves , 532 Cambridge University Press .
   Google Scholar
• Lee , HS and Tanaka , H . 2002 . “ Prediction of water level in a river mouth using neural network approach ” . In Proc. 13th IAHR-APD 669 – 674 .
   Google Scholar
• Liong , SY , Lim , WH and Paudyal , GN . 2000 . River stage forecasting in Bangladesh: Neural network approach . J. Comp. in Civ. Engrg., ASCE , 8 ( 2 ) : 201 – 220 .
   Google Scholar
• Monbaliu , J , Padilla , R , Osuna , P , Flather , R , Hargreaves , J , Carretero , JC , Espinar , S and Gunther , H . 1998 . WAM Shallow Waters Version 1.00, Code Changes to WAM Cycle 4.0 Documentation
   Google Scholar
• NOAA Product Information Catalog . 1988 . 171 Washington, DC : US Dept. of Commerce .
   Google Scholar
• Snyder , RL , Dobson , FW , Elliott , JA and Long , RB . 1981 . Array measurements of atmospheric pressure fluctuations above surface gravity waves . J. Fluid Mech. , 102 : 1 – 59 .
   Web of Science ®Google Scholar
• Specht , DF . 1991 . A general regression neural network . IEEE trans. Neural Networks , 2 ( 6 ) : 568 – 892 .
   PubMed Web of Science ®Google Scholar
• Supharatid , S . 2003 . Tidal-level forecasting and filtering by neural network model . Coastal Engineering Journal , 45 ( 1 ) : 119 – 138 .
   Web of Science ®Google Scholar
• Thirumalaiah , K and Deo , M . 1998 . River stage forecasting using artificial neural networks . J. Hydrol. Engng. , 3 ( 1 ) : 26 – 32 .
   Web of Science ®Google Scholar
• Tsai , CP and Lee , TL . 1999 . Back-propagation neural network in tidal-level forecasting . J. Wtrwy., Port, Coast., and Oc. Engrg., ASCE , 125 ( 4 ) : 195 – 202 .
   Web of Science ®Google Scholar
• Vaziri , M . 1997 . Prediction of Caspian sea surface water level by ANN and ARIMA models . J. Wtrwy., Port, Coast., and Oc. Engrg., ASCE , 123 ( 4 ) : 158 – 162 .
   Web of Science ®Google Scholar
• WAMDI group , Hasselmann , S , Hasselmann , K , Bauer , E , Janssen , PAEM , Komen , GJ , Bertotti , L , Lionello , P , Guillaume , A , Cardone , VC , Greenwood , JA , Reistad , M , Zambresky , L and Ewing , JA . 1988 . The WAM model, A third generation ocean wave prediction model . Journal of Physical Oceanography , 18 : 1775 – 1810 .
   Web of Science ®Google Scholar