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Investigation of the nonlinear effect of measured wind speed in predicting the height of wind waves
iman ‪Iman Esmaili Paeen Afrakoti‬ 4
1 Faculty of Marine Sciences and Oceanography, Khorramshahr university of Marine science and thechnology, Khorramshahr, Iran
3 Faculty of Marine Sciences, University of Mazandaran, Babolsar, Iran
4 Faculty of Electrical Engineering, Mazandaran University, Babolsar, Iran
Significant Wave Height (Hs) prediction is used in the analysis of marine systems including marine structural engineering and sediment transport. The Gulf of Mexico faces tropical storms shaped hurricane annually that affects the height of waves in this region, Therefore it is important to have precise estimation of the significant wave height. In this paper, we review previous studies and train artificial neural network model, to predict the effect of different wind speed powers and shear velocity in predicting the wave height in the next hours. The results showed that the presence of different wind speed powers increases the accuracy of predicting Hs relative to the wind shear speed. To increase the prediction accuracy, autocorrelation of the wave height data recorded in this region was used and a suitable model was presented. In this model was calculated power 2.3 of wind speed to predict the height of the next 3, 6 and 8 hours and power 1.9 to predict the height of the next 12 hours. Finally, the prediction results were compared with previous studies, and indicated the higher prediction accuracy.
[1] Wang J, Wang Y, Yang J. Forecasting of Significant Wave Height Based on Gated Recurrent Unit Network in the Taiwan Strait and Its Adjacent Waters. Water. 2021 Jan;13(1):86.
[2] Li M, Liu K. Probabilistic prediction of significant wave height using dynamic bayesian network and information flow. Water. 2020 Aug;12(8):2075.
[3] Zheng CW, Li CY. Variation of the wave energy and significant wave height in the China Sea and adjacent waters. Renewable and Sustainable Energy Reviews. 2015 Mar 1;43:381-7.
[4] Zheng CW, Wu GX, Chen X, Wang Q, Gao ZS, Chen YG, Luo X. CMIP5-based wave energy projection: case studies of the South China Sea and the East China Sea. IEEE Access. 2019 Jun 20;7:82753-63.
[5] US Army. Shore Protection Manual. 4th ed. US Army Engineer Waterways Experiment Station. Washington, DC: US Government Printing Office; 1984.
[6] US Army. Coastal Engineering Manual – Part II. No. 111 0-2-11 00. Washington, DC: US Army Corps of Engineers; 2015.
[7] Browne M, Castelle B, Strauss D, Tomlinson R, Blumenstein M, Lane C. Near-shore swell estimation from a global wind-wave model: Spectral process, linear, and artificial neural network models. Coastal Engineering. 2007 May 1;54(5):445-60.
[8] van der Westhuysen AJ, Zijlema M, Battjes JA. Nonlinear saturation-based whitecapping dissipation in SWAN for deep and shallow water. Coastal Engineering. 2007 Feb 1;54(2):151-70.
[9] Shamshirband S, Mosavi A, Rabczuk T, Nabipour N, Chau KW. Prediction of significant wave height; comparison between nested grid numerical model, and machine learning models of artificial neural networks, extreme learning and support vector machines. Engineering Applications of Computational Fluid Mechanics. 2020 Jan 1;14(1):805-17.
[10] Deo MC, Naidu CS. Real time wave forecasting using neural networks. Ocean engineering. 1998 Aug 1;26(3):191-203.
[11] Agrawal JD, Deo MC. On-line wave prediction. Marine structures. 2002 Jan 1;15(1):57-74.
[12] Makarynskyy O. Improving wave predictions with artificial neural networks. Ocean Engineering. 2004 Apr 1;31(5-6):709-24.
[13] Makarynskyy O, Pires-Silva AA, Makarynska D, Ventura-Soares C. Artificial neural networks in wave predictions at the west coast of Portugal. Computers & geosciences. 2005 May 1;31(4):415-24.
[14 Kazeminezhad MH, Etemad-Shahidi A, Mousavi SJ. Application of fuzzy inference system in the prediction of wave parameters. Ocean Engineering. 2005 Oct 1;32(14-15):1709-25.
[15] Mahjoobi J, Etemad-Shahidi A, Kazeminezhad MH. Hindcasting of wave parameters using different soft computing methods. Applied Ocean Research. 2008 Feb 1;30(1):28-36.
[16] Gaur S, Deo MC. Real-time wave forecasting using genetic programming. Ocean engineering. 2008 Aug 1;35(11-12):1166-72.
[17] Etemad-Shahidi A, Mahjoobi J. Comparison between M5′ model tree and neural networks for prediction of significant wave height in Lake Superior. Ocean Engineering. 2009 Nov 1;36(15-16):1175-81.
[18] Özger M. Significant wave height forecasting using wavelet fuzzy logic approach. Ocean Engineering. 2010 Nov 1;37(16):1443-51.
[19] Shahabi S, Khanjani MJ, Kermani MR. Significant wave height modelling using a hybrid Wavelet-genetic Programming approach. KSCE Journal of Civil Engineering. 2017 Jan;21(1):1-0.
[20] Duan WY, Han Y, Huang LM, Zhao BB, Wang MH. A hybrid EMD-SVR model for the short-term prediction of significant wave height. Ocean Engineering. 2016 Sep 15;124:54-73.
[21] Mafi S, Amirinia G. Forecasting hurricane wave height in Gulf of Mexico using soft computing methods. Ocean Engineering. 2017 Dec 1;146:352-62.
[22] Zamani A, Solomatine D, Azimian A, Heemink A. Learning from data for wind–wave forecasting. Ocean engineering. 2008 Jul 1;35(10):953-62.
[23] Kamranzad B, Etemad-Shahidi A, Kazeminezhad MH. Wave height forecasting in Dayyer, the Persian Gulf. Ocean engineering. 2011 Jan 1;38(1):248-55.
[24] Wikipedia [Internet]. 2017. Available from: https:// en.wikipedia.org/wiki/2016_Atlantic_hurricane_ season
[25] NOAA. national  data  buoy  center [Internet]. 2014. [cited 2014 March]. Available from: https://www.ndbc.noaa .gov/
]26[ منهاج محمدباقر. مبانی شبکه‌های عصبی. انتشارات دانشگاه صنعتی امیرکبیر؛ 1381.
[27] Beale MH, Hagan MT, Demuth HB. Neural network toolbox. User’s Guide, Math Works. 2010; 2:77-81.
[28] Deo MC, Jha A, Chaphekar AS, Ravikant K. Neural networks for wave forecasting. Ocean engineering. 2001 Jul 1;28(7):889-98.
[29] Londhe S, Panchang V. One-day wave forecasts using buoy data and artificial neural networks. In: Proceedings of OCEANS 2005 MTS/IEEE; 2005 Sep 17; Washington, DC, USA. IEEE;2006.
[30] Kumar NK, Savitha R, Al Mamun A. Ocean wave height prediction using ensemble of extreme learning machine. Neurocomputing. 2018 Feb 14;277:12-20.
دوره 6، شماره 2 - شماره پیاپی 11
اصل مقاله 1.03 MB
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تعداد مشاهده مقاله: 82
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احمدوند, همایون, نجارپور, محمد علی, اکبری نسب, محمد, & اسمعیلی پایین افراکتی, ایمان. (1399). بررسی اثر غیرخطی سرعت باد اندازه گیری شده در پیش بینی ارتفاع امواج ناشی از باد. هیدروفیزیک, 6(2), 71-83.
همایون احمدوند; محمد علی نجارپور; محمد اکبری نسب; ایمان اسمعیلی پایین افراکتی. "بررسی اثر غیرخطی سرعت باد اندازه گیری شده در پیش بینی ارتفاع امواج ناشی از باد". هیدروفیزیک, 6, 2, 1399, 71-83.
احمدوند, همایون, نجارپور, محمد علی, اکبری نسب, محمد, اسمعیلی پایین افراکتی, ایمان. (1399). 'بررسی اثر غیرخطی سرعت باد اندازه گیری شده در پیش بینی ارتفاع امواج ناشی از باد', هیدروفیزیک, 6(2), pp. 71-83.
احمدوند, همایون, نجارپور, محمد علی, اکبری نسب, محمد, اسمعیلی پایین افراکتی, ایمان. بررسی اثر غیرخطی سرعت باد اندازه گیری شده در پیش بینی ارتفاع امواج ناشی از باد. هیدروفیزیک, 1399; 6(2): 71-83.