Tsunami run-up height forecasting by using artificial neural networks

References

• Altun , F. , Kişi , Ö. and Aydin , K. 2008 . Predicting the compressive strength of steel fiber added lightweight concrete using neural network . Computational Materials Science , 42 ( 2 ) : 259 – 265 .
   Web of Science ®Google Scholar
• Andrade , C. , Borges , P. and Freitas , M. C. 2006 . Historical tsunami in the Azores archipelago (Portugal) . Journal of Volcanology and Geothermal Research , 156 ( 1–2 ) : 172 – 185 .
   Web of Science ®Google Scholar
• Atwater , B. F. 1987 . Evidence for Holocene earthquakes along the auter coast of Washington state . Science , 236 ( 4804 ) : 942 – 944 .
   PubMed Web of Science ®Google Scholar
• Atwater , B. F. and Hemphill-Haley , E. 1997 . Recurrence intervals for great earthquakes of the past 3,500 years at northeastern Willapa Bay, Washington (USGS Professional Paper 1576) , Reston, VA : United States Geological Survey .
   Google Scholar
• Atwater , B. F. 1995 . Summary of geologic evidence for past great earthquakes at the Cascadia subduction zone . Earthquake Spectra , 11 ( 1 ) : 1 – 18 .
   Google Scholar
• Barman , R. 2006 . Tsunami travel time prediction using neural networks . Geophysical Research Letters , 33 ( 16 ) L16612 (6 pages)
   PubMed Web of Science ®Google Scholar
• Beltrami , G. M . 2008 . An ANN algorithm for automatic, real-time tsunami detection in deep-sea level measurements . Ocean Engineering , 35 ( 5–6 ) : 572 – 587 .
   Web of Science ®Google Scholar
• Bishop , C. M. and Hinton , G. 1995 . Neural networks for pattern recognition , Oxford : Clarendon Press .
   Google Scholar
• Briggs . 1995 . “ Large scale three dimensional experiments of tsunami indundation ” . In Tsunami: progress in prediction, disaster prevention and warning , Edited by: Tsuchiya , Y. and Shuto , N. 129 – 149 . The Netherlands : Kluwer Academic Publisher .
   Google Scholar
• Bryant , E. 2008 . Tsunami: the underrated hazard , 2 , New York : Springer .
   Google Scholar
• Carrier , G. F. , Wu , T. T. and Yeh , H. 2003 . Tsunami run-up and draw-down on a plane beach . Journal of Fluid Mechanics , 475 : 79 – 99 .
   Web of Science ®Google Scholar
• Casey , K. , Lim , A. and Dozier , G. Evolving general regression neural networks for tsunami detection and response . Proceedings of the 2006 IEEE congress on evolutionary computations . Vancouver, Canada. pp. 2451 – 2458 .
   Google Scholar
• Chatterjee , A. 2000 . An introduction to the proper orthogonal decomposition . Current Science , 78 ( 7 ) : 808 – 817 .
   Web of Science ®Google Scholar
• Dao , M. H. , Tkalich , P. and Chan , E. S. 2008 . Tsunami forecasting using proper orthogonal decomposition method . Journal of Geophysical Research , 113 C06019(17 pages), doi:10.1029/2007JC004583
   Web of Science ®Google Scholar
• Dawson , A. G. and Stewart , I. 2007 . Tsunami deposits in the geological record . Sedimentary Geology , 200 ( 3–4 ) : 166 – 183 .
   Web of Science ®Google Scholar
• De Lange , W. P. and Healy , T. R. 2001 . Tsunami hazard for the Auckland region and Hauraki Gulf, New Zealand . NaturalHazards , 24 ( 3 ) : 267 – 284 .
   Google Scholar
• Fuhrman , D. R. and Madsen , P. A. 2008 . Simulation of nonlinear wave run-up with a high-order Boussinesq model . Coastal Engineering , 55 ( 2 ) : 139 – 154 .
   Web of Science ®Google Scholar
• Geist , E. 2005 . Local tsunami hazards in the Pacific Northwest from Cascadia subduction zone earthquakes (USGS professional paper 1661-B) , Reston, VA : US Geological Survey .
   Google Scholar
• Ghosh , A. K. 2008 . Assessment of earthquake-induced tsunami hazard at a power plant site . Nuclear Engineering and Design , 238 ( 7 ) : 1743 – 1749 .
   Web of Science ®Google Scholar
• Grilli , S. T. 1994 . Shoaling of solitary waves on plane beaches . Journal of Waterway, Port, Coastal and Ocean Engineering , 120 ( 6 ) : 609 – 628 .
   Web of Science ®Google Scholar
• Haykin , S. 1999 . Neural networks: a comprehensive foundation , 2 , Upper Saddle River, NJ : Prentice-Hall .
   Google Scholar
• Hsiao , S. C. 2008 . On the evolution and run-up of breaking solitary waves on a mild sloping beach . Coastal Engineering , 55 ( 12 ) : 975 – 988 .
   Web of Science ®Google Scholar
• Holder , L. 1985 . Multiple regression in hydrology , Wallingford : Institute of Hydrology .
   Google Scholar
• Hughes , S. A. 2004 . Estimation of wave run-up on smooth, impermeable slopes using the wave momentum flux parameter . Coastal Engineering , 51 ( 11–12 ) : 1085 – 1104 .
   Web of Science ®Google Scholar
• Johnston , D. 2005 . Measuring tsunami preparedness in coastal Washington, United States . Natural Hazards , 35 ( 1 ) : 173 – 184 .
   Web of Science ®Google Scholar
• Kalra , R. 2005 . RBF network for spatial mapping of wave heights . Marine Structures , 18 ( 3 ) : 289 – 300 .
   Web of Science ®Google Scholar
• Kanoglu , U. 2004 . Nonlinear evolution and runup-rundown of long waves over a sloping beach . Journal of Fluid Mechanics , 513 : 363 – 372 .
   Web of Science ®Google Scholar
• Kanoglu , U. and Synolakis , C. E. 1998 . Long wave runup on piecewise linear topographies . Journal of Fluid Mechanics , 374 : 1 – 28 .
   Web of Science ®Google Scholar
• Karunanithi , N. 1994 . Neural network for river flow prediction . Journal of Computing in Civil Engineering , 8 ( 2 ) : 201 – 219 .
   Web of Science ®Google Scholar
• Kim , D. H. , Cho , Y. S. and Yi , Y. K. 2007 . Propagation and run-up of nearshore tsunamis with HLLC approximate Riemann solver . Ocean Engineering , 34 ( 8–9 ) : 1164 – 1173 .
   Web of Science ®Google Scholar
• Kim , B. 2004 . Prediction of plasma etching using a randomized generalized regression neural network . Vacuum , 76 ( 1 ) : 37 – 43 .
   Web of Science ®Google Scholar
• Levenberg , K. 1944 . A method for the solution of certain problems in least squares . Quarterly of Applied Mathematics , 2 ( 2 ) : 164 – 168 .
   Google Scholar
• Li , Y. and Raichlen , F. 2001 . Solitary wave runup on plane slopes . Journal of Waterway, Port, Coastal and Ocean Engineering , 127 ( 1 ) : 33 – 44 .
   Web of Science ®Google Scholar
• Li , Y. and Raichlen , F. 2002 . Non-breaking and breaking solitary wave run-up . Journal of Fluid Mechanics , 456 : 295 – 318 .
   Web of Science ®Google Scholar
• Lin , P. , Chang , K.-A. and Liu , P. L.-F. 1999 . Run-up and run-down of solitary waves on sloping beaches . Journal of Waterway, Port, Coastal and Ocean Engineering , 125 ( 5 ) : 247 – 255 .
   Web of Science ®Google Scholar
• Liu , P. L.-F. 2005 . Observations by the International Tsunami Survey Team in Sri Lanka . Science , 308 ( 5728 ) : 1595
   PubMed Web of Science ®Google Scholar
• Liu , P. L.-F. , Wang , X. and Salisbury , A. J. 2009 . Tsunami hazard and early warning system in South China Sea . Journal of Asian Earth Sciences , 36 ( 1 ) : 2 – 12 .
   Web of Science ®Google Scholar
• Madsen , P. A. and Fuhrman , D. R. 2008 . Run-up of tsunamis and long waves in terms of surf-similarity . Coastal Engineering , 55 ( 3 ) : 209 – 223 .
   Web of Science ®Google Scholar
• Maiti , S. and Sen , D. 1999 . Computation of solitary waves during propagation and runup on a slope . Ocean Engineering , 26 ( 11 ) : 1063 – 1083 .
   Web of Science ®Google Scholar
• Marquardt , D. 1963 . An algorithm for least-squares estimation of nonlinear parameters . Journal of Applied Mathematics , 11 ( 2 ) : 431 – 441 .
   Web of Science ®Google Scholar
• Nadaraya , E. A. 1964 . On estimating regression . Theory of Probability and its Applications , 9 ( 1 ) : 141 – 142 .
   Google Scholar
• Namekar , S. , Yamazaki , Y. and Cheung , K. F. 2009 . Neural network for tsunami and runup forecast . Geophysical Research Letters , 36 L08604(6 pages), doi:10.1029/2009GL037184
   Web of Science ®Google Scholar
• Nelson , A. R. 2008 . Great-earthquake paleogeodesy and tsunamis of the past 2000 years at Alsea Bay, central Oregon coast, USA . Quaternary Science Reviews , 27 ( 7–8 ) : 747 – 768 .
   Web of Science ®Google Scholar
• Panizzo , A. and Briganti , R. 2007 . Analysis of wave transmission behind low-crested breakwaters using neural networks . Coastal Engineering , 54 ( 9 ) : 643 – 656 .
   Web of Science ®Google Scholar
• Parzen , E. 1962 . On estimation of a probability density function and mode . The Annals of Mathematical Statistics , 33 ( 3 ) : 1065 – 1076 .
   Google Scholar
• Pelinovsky , E. 1999 . Runup of tsunami waves on a vertical wall in a basin of complex topography . Physics and Chemistry of the Earth Part B-Hydrology Oceans and Atmosphere , 24 ( 5 ) : 431 – 436 .
   Web of Science ®Google Scholar
• Priest , G. 1995 . Explanation of mapping methods and use of the tsunami hazard maps of the Oregon coast (DOGAMI open-file report O-95-67) , Portland, OR : Oregon Department of Geology and Mineral Industries .
   Google Scholar
• Priest , G. Tsunami hazard assessment in Oregon . Proceedings of the International Tsunami Symposium, National Tsunami Hazard Mitigation Program Review . pp. 55 – 65 . Washington : Seattle .
   Google Scholar
• Principe , J. C. , Euliano , N. R. and Lefebvre , W. C. 1999 . Neural and adaptive systems: fundamentals through simulation , New York : Wiley .
   Google Scholar
• Romano , M. 2009 . Artificial neural network for tsunami forecasting . Journal of Asian Earth Sciences , 36 ( 1 ) : 29 – 37 .
   Web of Science ®Google Scholar
• Rumelhard , D. E. , Hinton , G. E. and Williams , R. J. 1986 . Learning internal representation by error backpropagation, parallel distributed processing: explorations microstructure of cognition , Vol. 1 , Cambridge, MA : MIT Press .
   Google Scholar
• Sanchez , A. and Cheung , K. F. 2007 . Tsunami forecast using an adaptive inverse algorithm for the Peru-Chile source region . Geophysical Research Letters , 34 ( 13 ) L13605(6 pages), doi:10.1029/2007GL030158
   Web of Science ®Google Scholar
• Schiøler , H. and Hartmann , U. 1992 . Mapping neural network derived from the Parzen window estimator . Neural Networks , 5 ( 6 ) : 903 – 909 .
   Web of Science ®Google Scholar
• Sharda , R. 1994 . Neural networks for the MS/OR analyst: An application bibliography . Interfaces , 24 ( 2 ) : 116 – 130 .
   Web of Science ®Google Scholar
• Specht , D. F. 1991 . A general regression neural network . IEEE Transactions on Neural Networks , 2 ( 6 ) : 568 – 576 .
   PubMed Web of Science ®Google Scholar
• Srivichai , M. , Supharatid , S. and Imamura , F. Developing of forecasted tsunami database along Thailand Andaman coastline . Proceeding, Asia Oceania Geosciences Society 3rd Annual Meeting . Singapore. pp. 138
   Google Scholar
• Synolakis , C. E. 1987 . The runup of solitary waves . Journal of Fluid Mechanics , 185 : 523 – 545 .
   Web of Science ®Google Scholar
• Synolakis , C. E. 2008 . Validation and verification of tsunami numerical models . Pure and Applied Geophysics , 165 ( 11–12 ) : 2197 – 2228 .
   Web of Science ®Google Scholar
• Tadepalli , S. and Synolakis , C. E. The run-up of N-waves on sloping beaches . Proceedings of the Royal Society of London Series A- Mathematical Physical and Engineering Sciences , Vol. 445 , pp. 99 – 112 . (1923)
   Google Scholar
• Titov , V. V. and Synolakis , C. E. 1998 . Numerical modeling of tidal wave runup . Journal of Waterway, Port, Coastal and Ocean Engineering , 124 ( 4 ) : 157 – 171 .
   Web of Science ®Google Scholar
• Titov , V. V. 2005 . Real-time tsunami forecasting: challenges and solutions . Natural Hazards , 35 ( 1 ) : 41 – 58 .
   Web of Science ®Google Scholar
• Tomandl , D. and Schober , A. 2001 . A modified general regression neural network (MGRNN) with new, efficient training algorithms as a robust ‘black box’-tool for data analysis . Neural Networks , 14 ( 8 ) : 1023 – 1034 .
   PubMed Web of Science ®Google Scholar
• Ward , S. N. 2001 . Landslide tsunami . Journal of Geophysical Research , 106 ( B6 ) : 11201 – 11215 .
   Web of Science ®Google Scholar
• Ward , S. N. 2002 . “ Tsunamis ” . In Encyclopedia of physical science and technology , Edited by: Meyers , R. A. Vol. 17 , 175 – 191 . New York : Academic Press .
   Google Scholar
• Watson , G. S. 1964 . Smooth regression analysis . Sankhya, Series A , 26 ( 4 ) : 359 – 372 .
   Google Scholar
• Wei , Y. 2003 . Inverse algorithm for tsunami forecasts . Journal of Waterway, Port, Coastal and Ocean Engineering , 129 ( 2 ) : 60 – 69 .
   Web of Science ®Google Scholar
• Widrow , B. , Rumelhart , D. E. and Lehr , M. A. 1994 . Neural networks: applications in industry, business and science . Communications of the ACM , 37 ( 3 ) : 93 – 105 .
   Web of Science ®Google Scholar
• Wood , N. 2007 . Variations in community exposure and sensitivity to tsunami hazards in Oregon (USGS scientific investigations report 2007-5283) , Reston, VA : United States Geological Survey .
   Google Scholar
• Wood , N. and Good , J. 2005 . Perceptions of earthquake and tsunami issues in US Pacific Northwest port and harbor communities . International Journal of Mass Emergencies and Disasters , 23 ( 3 ) : 103 – 138 .
   Google Scholar
• Wood , N. and Soulard , C. 2008 . Variations in community exposure and sensitivity to tsunami hazards on the open-ocean and Strait of Juan de Fuca coasts of Washington (USGS scientific investigations report 2008-5004) , Reston, VA : United States Geological Survey .
   Google Scholar
• Wu , J. K. 1994 . Neural networks and simulation methods , New York : Marcel Dekker .
   Google Scholar
• Yamazaki , Y. 2006 . Forecast of tsunamis from the Japan-Kuril-Kamchatka source region . Natural Hazards , 38 ( 3 ) : 411 – 435 .
   Web of Science ®Google Scholar
• Zelt , J. A. 1991 . The run-up of nonbreaking and breaking solitary waves . Coastal Engineering , 15 ( 3 ) : 205 – 246 .
   Web of Science ®Google Scholar
• Zhang , G. , Patuwo , B. E. and Hu , M. Y. 1998 . Forecasting with artificial neural networks: the state of the art . International Journal of Forecasting , 14 ( 1 ) : 35 – 62 .
   Web of Science ®Google Scholar