Application of neural networks and support vector machine for significant wave height prediction

• Autorzy: Berbić J. , Ocvirk E. , Carević D. , Lončar G.

Identyfikatory

DOI

10.1016/j.oceano.2017.03.007

• Języki publikacji: EN

Abstrakty

EN

For the purposes of planning and operation of maritime activities, information about wave height dynamics is of great importance. In the paper, real-time prediction of significant wave heights for the following 0.5–5.5 h is provided, using information from 3 or more time points. In the first stage, predictions are made by varying the quantity of significant wave heights from previous time points and various ways of using data are discussed. Afterwards, in the best model, according to the criteria of practicality and accuracy, the influence of wind is taken into account. Predictions are made using two machine learning methods – artificial neural networks (ANN) and support vector machine (SVM). The models were built using the built-in functions of software Weka, developed by Waikato University, New Zealand.

Słowa kluczowe

EN

significant wave height; wave prediction; machine learning; ANN; artificial neural networks; SVM; support vector machine

• Wydawca: Polish Academy of Sciences, Institute of Oceanology ; Elsevier
• Czasopismo: Oceanologia
• Rocznik: 2017
• Tom: No. 59 (3)
• Strony: 331--349
• Opis fizyczny: Bibliogr. 54 poz., fot., rys., tab., wykr.

Twórcy

autor

Berbić J.

• Croatian Hydrological and Meteorological Service, Zagreb, Croatia

autor

Ocvirk E.

• The Faculty of Civil Engineering, University of Zagreb, Zagreb, Croatia

autor

Carević D.

• The Faculty of Civil Engineering, University of Zagreb, Zagreb, Croatia

autor

Lončar G.

• The Faculty of Civil Engineering, University of Zagreb, Zagreb, Croatia

Bibliografia

• [1] Abed-Elmdoust, A., Kerachian, R., 2012. Wave height prediction using the rough set theory. Ocean Eng. 54, 244-250, http://dx.doi.org/10.1016/j.oceaneng.2012.07.020.
• [2] Altunkaynak, A., 2013. Prediction of significant wave height using geno-multilayer perceptron. Ocean Eng. 58, 144-153, http://dx.doi.org/10.1016/j.oceaneng.2012.08.005.
• [3] Altunkaynak, A., Wang, K.-H., 2012. Estimation of significant wave height in shallow lakes using the expert system techniques. Expert Syst. Appl. 39 (3), 2549-2559, http://dx.doi.org/10.1016/j.eswa.2011.08.106.
• [4] Andročec, V., Beg-Paklar, G., Dadić, V., Djakovac, T., Grbec, B., Janeković, I., Krstulović, N., Kušpilić, G., Leder, N., Lončar, G., Marasović, I., Precali, R., Šolić, M., 2009. The Adriatic Sea Monitoring Program —final report, Zagreb.
• [5] Balas, C. E., Koc, L., Balas, L., 2004. Predictions of missing wave data by recurrent neuronets. J. Waterw. Port Coastal Ocean Eng. 130 (5), 256-266, http://dx.doi.org/10.1061/(ASCE)0733-950X(2004)130:5(256).
• [6] Bell, B., Wallace, B., Zhang, D., 2012. Forecasting river runoff through support vector machines. In: 2012 IEEE 11th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC), http://dx.doi.org/10.1109/ICCI-CC.2012.6311127.
• [7] Berbić, J., Ljubičić, I., Štimac, J., 2015. Usporedba raznih metoda rješavanja problema nedostajućih vrijednosti kod klasifikacije. [available: 29.06.2016], https://web.math.pmf.unizg.hr/nastava/su/projektni-zadaci/.
• [8] Bretschneider, C. L., 1952. The generation and decay of wind waves in deep water. Trans. Am. Geophys. Union 33 (3), 381-389, http://dx.doi.org/10.1029/TR033i003p00381.
• [9] Brzović, N., 1999. Factors affecting the Adriatic cyclone and associated windstorms. Contrib. Atmos. Phys. 72, 51-65.
• [10] Brzović, N., Strelec-Mahović, N., 1999. Cyclonic activity and severe jugo in the Adriatic. Phys. Chem. Earth (B) 24, 653-657.
• [11] Bubnova, R., Horanyi, A., Malardel, S., 1993. International project ARPEGE/ALADIN. EWGLAM Newsletter 22, 117-130.
• [12] Cavaleri, L., Malanotte-Rizzoli, P., 1981. Wind wave prediction in shallow water: theory and application. J. Geophys. Res. 86 (C11), 10961-10973, http://dx.doi.org/10.1029/JC086iC11p10961.
• [13] Chandra, P., Deka, P., Prahlada, R., 2012. Discrete wavelet neural network approach in significant wave height forecasting multistep lead time. Ocean Eng. 43, 32-42, http://dx.doi.org/10.1016/j.oceaneng.2012.01.017.
• [14] Cordoneanu, E., Geleyn, J. F., 1998. Application to local circulation above the Carpathian-Black Sea area of a NWP-type meso-scale model. Contrib. Atmos. Phys. 71, 191-212.
• [15] Courtier, P.C., Freydier, J.F., Geleyn, F., Rochas, M., 1991. The ARPEGE project at METEO-FRANCE. Proc. ECMWF workshop “Numerical methods in atmospheric models” 9-13 (2), 193-231.
• [16] Deo, M. C., Jha, A., Chaphekar, A. S., Ravikant, K., 2001. Neural networks for wave forecasting. Ocean Eng. 28 (7), 889-898, http://dx.doi.org/10.1016/S0029-8018(00)00027-5.
• [17] Deo, M. C., Naidu, C. S., 1999. Real time wave forecasting using neural networks. Ocean Eng. 26 (3), 191-203, http://dx.doi.org/10.1016/S0029-8018(97)10025-7.
• [18] Donelan, M. A., 1977. A simple numerical model for wave and wind stress prediction. National Water Res. Inst. Manuscript, Berlington, Ontario, Canada, 28 pp.
• [19] Etemad-Shahidi, A., Mahjoobi, J., 2009. Comparison between M50 model tree and neural networks for prediction of significant wave height in Lake Superior. Ocean Eng. 36 (15-16), 1175-1181, http://dx.doi.org/10.1016/j.oceaneng.2009.08.008.
• [20] Fernandez, J. C., Salcedo-Sanz, S., Gutiérrez, P. A., Alexandre, E., Hervás-Martínez, C., 2015. Significant wave height and Energy flux range forecast with machine learning classifiers. Eng. Appl. Artif. Intell. 43, 44-53, http://dx.doi.org/10.1016/j.engap-pai.2015.03.012.
• [21] Goda, Y., 2000. Random Seas and Design of Maritime Structures. Advanced Series on Ocean Engineering 15. World Scientific Publishing Company, Singapore, 732 pp.
• [22] Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I. H., 2009. The WEKA Data Mining Software: An Update. ACM SIGKDD Explorations Newslett. 11 (1), 10-18, http://dx.doi.org/10.1145/1656274.1656278.
• [23] Herbich, J. B., Bretschneider, C. L., 1990. Handbook of Coastal and Ocean Engineering, vol. 1. Gulf Publ. Co., London, 1192 pp.
• [24] Herzbach, H., Janssen, P. A. E. M., 1999. Improvement of the shortfetch behaviour in the Wave Ocean Model (WAM). J. Atmos. Oceanic Technol. 16, 884-892.
• [25] Ivatek-Sahdan, S., Tudor, M., 2004. Use of high-resolution dynamical adaptation in operational suite and research impact studies. Meteorol. Z. 13, 99-108.
• [26] Jain, P., Deo, C., 2006. Neural networks in ocean engineering. Ships Offshore Struct. 1 (1), 25-35, http://dx.doi.org/10.1533/saos.2004.0005.
• [27] Janssen, P. A. E. M., 1989. Wave induced stress and drag of airflow over sea waves. J. Phys. Oceanogr. 19, 745-754.
• [28] Janssen, P. A. E. M., 1991. Quasi-linear theory of wind wave generation applied to wave forecasting. J. Phys. Oceanogr. 21 (11), 1631-1642, http://dx.doi.org/10.1175/1520-0485(1991)021%3C1631:QLTOWW%3E2.0.CO;2.
• [29] Janssen, P. A. E. M., 1992. Experimental evidence of the effect of surface waves on the airflow. J. Phys. Oceanogr. 22 (12), 1600-1604, http://dx.doi.org/10.1175/1520-0485(1992)022%3C1600:EEOTEO%3E2.0.CO;2.
• [30] Janssen, P. A. E. M., 1998. On the effect of ocean waves on the kinetic energy balance and consequences for the inertial dissipation technique. J. Phys. Oceanogr. 29 (3), 530-534, http://dx.doi.org/10.1175/1520-0485(1999)029<0530:OTEOOW>2.0.CO;2.
• [31] Johnson, H. K., 1998. On modelling wind-waves in shallow and fetch limited areas using method of Holthuijsn, Booij and Herbers. J. Coast. Res. 14 (3), 917-932.
• [32] Johnson, H. K., Kofoed-Hansen, H., 2000. Influence of bottom friction on sea surface roughness and its impact on shallow water wind wave modelling. J. Phys. Oceanogr. 30, 1743-1756.
• [33] Kahma, K. K., Calkoen, C. J., 1992. Reconciling discrepancies in the observed growth of wind-generated waves. J. Phys. Oceanogr. 22 (12), 1389-1405, http://dx.doi.org/10.1175/1520-0485(1992)022%3C1389:RDITOG%3E2.0.CO;2.
• [34] Komen, G. J., Cavaleri, M., Donelan, K., Hasselman, S., Hasselman, K., Janssen, P. A. E. M., 1994. Modelling of Dynamic of Ocean Surface Waves. Cambridge Univ. Press, Cambridge, 532 pp.
• [35] Lamb, H., 1932. Hydrodynamics, 6th edn. Dover Publ., New York, 768 pp.
• [36] MacKay, D. J. C., 2003. Information Theory, Inference, and Learning Algorithms. Cambridge Univ. Press, Cambridge, 640 pp.
• [37] Mahjoobi, J., Mosabbeb, A. E., 2009. Prediction of significant wave height using regressive support vector machines. Ocean Eng. 36 (5), 339-347, http://dx.doi.org/10.1016/j.oceaneng.2009.01.001.
• [38] Makarynskyy, O., 2004. Improving wave predictions with artificial neural networks. Ocean Eng. 31 (5-6), 709-724, http://dx.doi.org/10.1016/j.oceaneng.2003.05.003.
• [39] Malekmohamadi, I., Bazargan-lari, M. R., Kerachian, R., Reza, M., 2011. Evaluating the efficacy of SVMs, BNs, ANNs and ANFIS in wave height prediction. Ocean Eng. 38 (2-3), 487-497, http://dx.doi.org/10.1016/j.oceaneng.2010.11.020.
• [40] Medina, J. R., 2005. Letter to the editor. Improving wave predictions with artificial neural networks, by O. Makarynskyy. Ocean Eng. 32, 101-103.
• [41] Miles, O., 1957. On the generation of surface waves by shear flows. J. Fluid Mech. 3 (2), 185-204, http://dx.doi.org/10.1017/S0022112057000567.
• [42] Mitchell, T. M., 1997. Machine Learning. McGraw Hill Inc., New York, USA, 432 pp.
• [43] Ng, A., 2015. Lectures: Machine Learning. Stanford University, [accessed on: 03.09.2015], https://www.coursera.org/learn/machine-learning.
• [44] Nitsure, S. P., Londhe, S. N., Khare, K. C., 2012. Wave forecasts using wind information and genetic programming. Ocean Eng. 54 (1), 61-69, http://dx.doi.org/10.1016/j.oceaneng.2012.07.017.
• [45] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Duborg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, É., 2011. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825-2830.
• [46] Phillips, O., 1957. On the generation of waves by turbulent wind. J. Fluid Mech. 2 (5), 417-445, http://dx.doi.org/10.1017/S0022112057000233.
• [47] Reikard, G., Pinson, P., Bidlot, J.-R., 2011. Forecasting ocean wave energy: the ECMWF wave model and time series methods. Ocean Eng. 38 (10), 1089-1099, http://dx.doi.org/10.1016/j.ocea-neng.2011.04.009.
• [48] Russell, S., Norvig, P., 2010. Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, USA, 1152 pp.
• [49] Schwab, D. J., Bennett, J. R., Liu, P. C., Donelan, M. A., 1984. Application of a simple numerical wave prediction model to lake Erie. J. Geophys. Res. 89 (C3), 3586-3592, http://dx.doi.org/10.1029/JC089iC03p03586.
• [50] Smola, A. J., Schölkopf, B., 2004. A tutorial on support vector regression. Stat. Comput. 14, 199-222. [accessed on: 29.06.2016], https://alex.smola.org/papers/2004/SmoSch04.pdf.
• [51] Sverdrup, H. V., Munk, W. H., 1946. Empirical and theoretical relations between wind, sea and swell. Trans. Am. Geophys. Union 27 (6), 823-827, http://dx.doi.org/10.1029/TR027i006p00823.
• [52] Sylaios, G., Bouchette, F., Tsihrintzis, V. A., Denamiel, C., 2009. A fuzzy interference system for wind-wave modeling. Ocean Eng. 36 (17-18), 1358-1365, http://dx.doi.org/10.1016/j.ocea-neng.2009.08.016.
• [53] Tsai, C.-P., Lin, C., Shen, J.-N., 2002. Neural network for wave forecasting among multi-stations. Ocean Eng. 29 (13), 1683-1695.
• [54] Zamani, A., Solomatine, D., Azimian, A., Heemink, A., 2008. Learning from data for wind—wave forecasting. Ocean Eng. 35 (10), 953-962, http://dx.doi.org/10.1016/j.oceaneng.2008.03.007.