Forecasting of Wind-Wave Height by Using Adaptive Neuro-Fuzzy Inference System and Decision Tree

Document Type : Regular Article

Authors

1 Graduate Master of Science of Coastal, Port and Marine Science Engineering, Khorramshahr University of Marine Science and Technology, Khorramshahr, Iran

2 Assistant Professor, Khorramshahr University of Marine Science and Technology, Khorramshahr, Iran

Abstract

Wind-induced waves are considered to be the most important waves in the sea due to their high energy and frequency. Among the characteristics of the waves, height is one of the most important parameters that are used in most equations related to marine engineering designs. Since the application of soft computing methods in marine engineering has been developed in recent years, in present research, an adaptive neuro-fuzzy inference system and a decision tree have been used to predict the wind-induced wave height in Bushehr port. In order to identify the effective parameters, implementing different models from different inputs. By considering the accuracy of the models, the effective parameters in wave height were identified using statistical measures correlation coefficient (r), Mean Square Error (MSE). The final results of this study showed that in the prediction of wind-induced wave height, compared to the decision tree, the accuracy of the model of the neural-fuzzy system for 3, 6 and 9 hours was higher. Also, the results showed that the use of wind shear velocity instead of wind speed at 10 meters above the water level had a higher accuracy in forecasting of the significant wave height. The results also indicated that among the presented models, the combined model of the significant wave height, shear velocity, and the difference between the direction and wind speed as well as the length of the fetch has the highest accuracy.

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[1]       Stoyan G. Phillips, O. M., The Dynamics of the Upper Ocean. 2nd edition. Cambridge, Cambridge University Press 1980. VII, 336 S., £ 7.95 A P/B. ISBN 0-521-29801-6. ZAMM - J Appl Math Mech / Zeitschrift Für Angew Math Und Mech 2008;61:274–274. doi:10.1002/zamm.19810610617.
[2]       Taleghani M, Amir teimori A. Projection of Caspian Sea Waves Using Artificial Neural Network. J Appl Math 2008;5:39–47.
[3]       Kamranzad B, A ES. Prediction of wind waves in Assaluyeh using numerical model. 2011.
[4]       Mavedatnia H, Bakhtiari M, H B, Behdarvandi Asgar M. Prediction of Wave Characteristics in Imam Khomeini Port Using Artificial Neural Network Software. 2014.
[5]       Nitsure SP, Londhe SN, Khare KC. Prediction of sea water levels using wind information and soft computing techniques. Appl Ocean Res 2014;47:344–51. doi:10.1016/j.apor.2014.07.003.
[6]       Kurniawan A, Ooi SK, Babovic V. Improved sea level anomaly prediction through combination of data relationship analysis and genetic programming in Singapore Regional Waters. Comput Geosci 2014;72:94–104. doi:10.1016/j.cageo.2014.07.007.
[7]       Akpınar A, Özger M, Kömürcü Mİ. Prediction of wave parameters by using fuzzy inference system and the parametric models along the south coasts of the Black Sea. J Mar Sci Technol 2014;19:1–14. doi:10.1007/s00773-013-0226-1.
[8]       Londhe SN, Shah S, Dixit PR, Nair TMB, Sirisha P, Jain R. A Coupled Numerical and Artificial Neural Network Model for Improving Location Specific Wave Forecast. Appl Ocean Res 2016;59:483–91. doi:10.1016/j.apor.2016.07.004.
[9]       Nagalingam K, Ramasamy S, Mamun AAM. Regional ocean wave height prediction using sequential learning neural networks. J Ocean Eng 2016;129:605–12.
[10]     Hashim R, Roy C, Motamedi S, Shamshirband S, Petković D. Selection of climatic parameters affecting wave height prediction using an enhanced Takagi-Sugeno-based fuzzy methodology. Renew Sustain Energy Rev 2016;60:246–57. doi:10.1016/j.rser.2016.01.098.
[11]     Tur R, Soylu Pekpostalci D, Arlı Küçükosmanoğlu Ö, Küçükosmanoğlu A. Prediction of Significant Wave Height along Konyaaltı Coast. Int J Eng Appl Sci 2017;9:106–14. doi:10.24107/ijeas.368922.
[12]     Mohammad Beigi Kasvaei M, Kazeminezhad MH, Yeganeh-Bakhtiary A. Numerical Study on Wave Induced Flow Field around a Vibrant Monopile Regarding Cross-Sectional Shape. Int J Coast Offshore Eng 2019;3:1–9. doi:10.29252/ijcoe.3.2.1.
[13]     Akbarinasab M, Paeen Afrakoti I. Application of Soft Computing in Forecasting wave height (Case study: Anzali). Int J Coast Offshore Eng 2019;3:31–40.
[14]     Sayehbani M. Numerical Modeling of Wave and Current Patterns of Beris Port in East of Chabahar-Iran. Int J Coast Offshore Eng 2019;3:21–9.
[15]     Zamani A, Solomatine D, Azimian A, Heemink A. Learning from data for wind–wave forecasting. Ocean Eng 2008;35:953–62. doi:10.1016/j.oceaneng.2008.03.007.
[16]     Donelan MA. Similarity theory applied to the forecasting of wave heights, periods and directions. In: Proceeding of the Canadian Coastal Conference, National Research Council of Canada. 1980.
[17]     Deo MC, Jha A, Chaphekar AS, Ravikant K. Neural networks for wave forecasting. Ocean Eng 2001;28:889–98. doi:10.1016/S0029-8018(00)00027-5.
[18]     Kamranzad B, Etemad-Shahidi A, Kazeminezhad MH. Wave height forecasting in Dayyer, the Persian Gulf. Ocean Eng 2011;38:248–55. doi:10.1016/j.oceaneng.2010.10.004.
[19]     Derakhshan S, Mostafa Gharebaghi, A Chanaghloo M. Projection of sea waves with experimental methods in Bushehr area. 1th Natl Civ Eng Conf Sharif Univ Technol Iran 2004.