Wave simulation and forecasting using wind time history and data-driven methods

Ships and Offshore Structures

Volumn 5, Issue 3, 2010, Pages 253-266

  Kambekar, A R ^a   Deo, M C ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

Author keywords

Genetic programming; Model trees; Wave forecasting; Wave simulation; Wind time history

Indexed keywords

BUOY MEASUREMENTS; DATA-DRIVEN METHODS; DEVELOPED MODEL; EAST COAST; MODEL TREES; OFFSHORE LOCATIONS; REAL TIME; SCATTER DIAGRAMS; SIGNIFICANT WAVE HEIGHT; TESTING SETS; TIME HISTORY; TIME SERIES FORECASTING; WAVE FORECASTING; WAVE PERIOD; WAVE SIMULATION; WAVE SIMULATIONS; WIND DIRECTIONS; WIND SPEED;

CONCRETE BRIDGES; ERROR STATISTICS; FORECASTING; GENETIC PROGRAMMING; LOCATION; TIME SERIES; TREES (MATHEMATICS);

COMPUTER SIMULATION;

EID: 78651585866     PISSN: 17445302     EISSN: None     Source Type: Journal    
DOI: 10.1080/17445300903439223     Document Type: Article

References (60)

1. Agrawal JD, Deo MC. 2002. Online wave prediction. Mar Struct. 15(1):57-74.
2. Altunkaynak A. 2007. Adaptive estimation of wave parameters by Geno-Kalman filtering. Ocean Eng. 35(11-12):1245-1251.
3. Andreas EL, Wang S. 2007. Predicting significant wave height of theNortheast coast of theUnited States. Ocean Eng. 34:1328-1335.
4. Bhattacharya B, Deibel IK, Karstens SAM, Solomatine DP. 2003. Neural networks in sedimentation modeling for the approach channel of the port area of Rotterdam. Proceedings of the7th International Conference on Near shore and Estuarine Cohesive Sediment Transport Processes (INTERCOH); Virginia Institute of Marine Science, VA, USA.
5. Bhattacharya B, Price RK, Solomatine DP. 2004. A data mining approach to modeling sediment transport. Proceedings of Sixth International Conference on Hydroinformatics. Singapore: World Scientific. p. 1663-1670.
6. Bhattacharya B, Solomatine DP. 2005. Neural networks and M5 model trees in modeling water level-discharge relationship. Neurocomputing. 63:381-396.
7. Bhattacharya B, Solomatine DP. 2006. Machine learning in sedimentation modeling. J Neural Netw. 19(2006):208-214.
8. Bishop CT. 1983. Comparison of manual wave prediction models. Journal of the Waterway Port Coastal and Ocean Division 109(1):1-17.
9. Bishop CT, Donelan MA, Kahma KK. 1992. Shore protection manual's wave prediction (reviewed). Coast Eng. 17:25-48.
10. Bretchneider CL. 1970. Forceasting relations for wave generation. Look/Lab Hawaii 1(3), August 1970.
11. Browne M, Castelle B, Strauss D, Tomilnson R, Blumenstein M, Lane C. 2007. Nearshore swell estimation from a global wind-wave model spectral process, linear and artificial neural network models. Coast Eng. 54:445-460.
12. Charhate SB and Deo MC. 2007. Application of genetic programming to solve coastal and ocean engineering problems, Proceedings of the International Workshop on Advances in Hydroinformatics, Niagara Falls, Canada, June 4-7, 2007, A1, 1-14.
13. Charhate SB, Deo MC, Londhe SN. 2008. Inverse modeling to derive wind parameters from wave measurements. Appl Ocean Res.30:120-129.
14. Charhate SB, Deo MC, Londhe SN. 2009. Genetic programming for real-time prediction of offshore wind. Ships Offshore Struct. 1754-212X. 4(1):77-88.
15. Charhate SB, Deo MC, Sanilkumar V. 2007. Soft and hard computing approaches for real time prediction of currents in a tide dominated area. J. Eng. Maritime Environ. Proceedings of the Institution of Mechanical Engineers; London, Part M, 221/2007, p. 147-163.
16. Coastal Engineering Manual. 2003. Meteorology and Wave Climate, Engineer Manual; EM 1110-2-1100,Washington (DC): US Army Corps of Engineers.
17. Daga M, Deo MC. 2008. Alternative data-driven methods to estimate wind from waves by inverse modeling. Vol 49, Nat Hazards. The Netherlands: Springer; p. 293-310.
18. de Vos NJ, Rientjes THM. 2005. Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation. Hydrol. Earth Syst. Sci. Discuss. 2:365-415. Available from: www.copernicus.org/EGU/hess/hessd/2/365.
19. Deo MC, Jha A, Chaphekar AS, Ravikant K. 2001. Wave prediction using neural networks. Vol. 28, Ocean Eng. Oxford (UK): Elsevier, p. 889-898.
20. Deo MC, Naidu CS. 1999. Real-time wave forecasting using neural networks. Vol. 26 Ocean Eng. Oxford (UK): Elsevier, p. 191-203.
21. Donelan MA. 1980. Similarity theory applied to the forecasting of wave heights, periods and directions. Proceedings of Canadian Coastal Conference, National Research Council of Canada. Burlington, Ontario: National Research Council. p. 47-61.
22. Drecourt JP. 1999. Application of neural networks and genetic programming to rainfall runoff modeling. Danish Hydraulic Institute (Hydro-Informatics Technologies HIT. June. D2K- 0699-1.
23. Drunpob A, Chang NB, Beaman M. 2005. Stream flow rate prediction using genetic programming in a semi-arid coastal watershed. Proceedings World Water and Environmental Resources Congress; 2005 May 15-19; Alaska, USA.
24. Gaur S, Deo MC. 2008. Real time wave forecasting using Genetic programming. Ocean Eng.35:1166-1172.
25. Goda Y. 2003. Revisiting Wilson's formulas for simplified wind-wave prediction. ASCE J. Waterway Port Coast. Ocean Eng. 129(2):93-95.
26. Gunaydin A. 2008. The estimation of monthly mean significant wave heights by using artificial neural network and regression methods. Ocean Eng. 35:1406-1415.
27. Ho PC, Yim JZ. 2006. Wave height forecasting by the transfer function model. Ocean Eng. 33:1230-1248.
28. Jain P, Deo MC. 2007. Real time wave forecasts off western Indian coast. Appl Ocean Res. 29:72-79.
29. Jain P, Deo MC. 2008. Artificial intelligence tools to forecast ocean waves in real time. Open Ocean Eng. J. 1, 13-21.
30. Janssen P. 2008. Progress in wave forecasting. J. Comput. Phys. 227(7): 3572-3594.
31. Kalra R, Deo MC. 2008. Genetic Programming to retrieve missing information in wave records along the west coast of India. Appl. Ocean Res. 29(3):99-111.
32. Kalra R, Deo MC, Kumar R, Agarwal VK. 2008. Genetic programming to estimate coastal waves from deep water measurements. Int. J. Ecol. Dev. 10(S08): 67-76.
33. Kanbua W, Supharatidb S, I-Ming T. 2005. Ocean wave forecasting in the Gulf of Thailand during typhoon Linda 1997: WAM and neural network approaches, Science Asia. [J. Sci. Soc. Thailand]. Science Asia Journal 31:243-250.
34. Karunanithi N, Greeney WJ, Whitley D, Bovee K. 1994. Neural network for river flow prediction. ASCE J Comput. Civil Eng. 8(2):201-219.
35. Kazeminezhad MH, Etemad-Shahidi A, Mousavi SJ. 2005. Application of fuzzy inference system in the prediction of wave parameters. Ocean Eng. 32:1709-1725.
36. Kobayashi T, Yasuda T. 2004. Nearshore wave prediction by coupling a wave model and statistical methods. Coastal Eng. 51:297-308.
37. Komen GJ, Cavaleri L, Donelan M, Hasselmann K, Hasselmann S, Janssen PAEM, editors. 1994. Dynamics and modeling of ocean waves. Cambridge: Cambridge University Press.
38. Koza. 1992. Genetic Programming-on the programming of computers by means of natural selection. Cambridge, MA: MIT Press.
39. Krasnopolsky VM, Chalikov DV, Tolman HL. 2002. A neural network technique to improve computational efficiency of numerical oceanic models. Ocean Eng. 4(3-4):363-383.
40. Londhe SN. 2008. Soft computing approach for real-time estimation of missing wave heights. Ocean Eng. 35:1080-1089.
41. Mahjoobi J, Shahidi E, Kazeminezhad MH. 2008. Hindcasting of wave parameters using different soft computing methods. J. Appl. Ocean Res. 30:28-36.
42. Makarynskyy O. 2004. Improving wave predictions with artificial neural networks. Ocean Eng. 31(5-6):709-724.
43. Makarynskyy O, Makarynska D. 2006. Wave prediction and data supplementation using artificial neural networks. J. Coast. Res. 22:146-155.
44. Makarynskyy O, Pires-Silva AA, Makarynskyy D, Ventura-Soares C. 2005. Artificial neural networks in wave predictions at the west coast of Portugal. Comput. Geosci. 31(4):415-424.
45. Malekmohamadi I, Ghiassi R, Yazdanpanah MJ. 2008.Wave hind-casting by coupling numerical model and artificial neural networks. Ocean Eng. 35(3-4):417-425.
46. Muttil N, Liong SY. 2004. A superior exploration-exploitation balance in shuffled complex evolution. ASCE J. Hydraul. Eng. 30(12):1202-1205.
47. Quinlan JR. 1992. Learning with continuous classes. Proceedings of Australian Joint Conference on AI. Singapore: World Scientific. p. 343-348.
48. Sakhare S, Deo MC. 2009. Derivation of wave spectrum using data driven methods. Mar. Struct. 22(3): 594-609.
49. Shore Protection Manual. 1984. Washington (DC): US Army Coastal Engineering Research Center.
50. Solomatine DP, Dulal KN. 2003. Model tree as an alternative to neural network in rainfall runoff modeling. Hydrol. Sci. J. 48(3):399-412.
51. Solomatine DP, Xue Y. 2004. M5 model trees compared to neural networks, application of flood forecasting in the upper reach of the Huai River in China. ASCE J. Hydrol. Eng. 9(6):491-501.
52. Tokar AS, Johnson PA. 1999. Rainfall-runoff modeling using artificial neural networks. J. Hydrol. Eng. 4(3):232-239.
53. Tokar AS, Markus M. 2000. Precipitation runoff modeling using artificial neural network and conceptual models. ASCE J. Hydrol. Eng. 5(2):156-161.
54. Ustoorikar K, Deo MC. 2008. Filling up gaps in wave data with genetic programming. Mar Struct. 21:177-195.
55. WAMDI Group. 1988. The WAM model-a third generation wave prediction model. J. Phys. Oceanogr. 18:1775-1810.
56. Whigham PA, Crapper PF. 2001. Modeling rainfall-runoff using genetic programming. Math.Comput.Modeling. 33:707-721.
57. Wilson BW. 1965. Numerical prediction of ocean waves in the North Atlantic for December 1959. Deutsche Hydrogr. A. 18(3):113-130.
58. Witten IH, Frank E. 2000. Datamining: practical machine learning tools and techniques. San Francisco (CA):Morgan Kaufmann.
59. Zamani A, Solomatine D, Azimian A, Heemink A. 2008. Learning from data for wind wave forecasting. J. Ocean Eng. 35:953-962.
60. Zhang QJ, Cao Y, Xu JJ, Devabhaktuni VK, Ding RT. 2006. An adjoint sensitivity technique for dynamic neural network modeling and design of high-speed interconnect. Int. J. RFMicrow. CAE. 16(4):385-399.