1. Bretschneider, C. L., (1951). Revised wave forecasting relationships. Coastal Engineering Proceedings, 1(2), 1. [ DOI:10.9753/icce.v2.1] 2. Sverdrup, H. U., & Munk, W. H.,(1947), Wind, sea, and swell: theory of relations for forecasting. [ DOI:10.5962/bhl.title.38751] 3. Army, U. S., (2003), Coastal engineering manual, chapter II2, meteorology and wave climate. US Army Engineer Waterways Experiment Station, US Government Printing Office, No. EM, 11101112. 4. Donelan, M. A., (1980). Similarity theory applied to the forecasting of wave heights, periods and directions. National Water Research Institute. 5. Booij, N., Ris, R. C., and Holthuijsen, L. H., (1999), A third‐generation wave model for coastal regions: 1. Model description and validation. Journal of Geophysical Research: Oceans, 104(C4), 76497666. [ DOI:10.1029/98JC02622] 6. Komen, G. J., Cavaleri, L., and Donelan, M., (1996), Dynamics and modelling of ocean waves. Cambridge university press. 7. Goda, Y., (2003), Revisiting Wilson's formulas for simplified windwave prediction. Journal of Waterway, Port, Coastal, and Ocean Engineering, 129(2), 9395. [ DOI:10.1061/(ASCE)0733950X(2003)129:2(93)] 8. Mahjoobi, J., EtemadShahidi, A., and Kazeminezhad, M. H., (2008), Hindcasting of wave parameters using different soft computing methods. Applied Ocean Research, 30(1), 2836. [ DOI:10.1016/j.apor.2008.03.002] 9. Makarynskyy, O., (2004), Improving wave predictions with artificial neural networks. Ocean Engineering, 31(56), 709724. [ DOI:10.1016/j.oceaneng.2003.05.003] 10. Tsai, C.P., & Lee, T.L., (1999), Backpropagation neural network in tidallevel forecasting. Journal of Waterway, Port, Coastal, and Ocean Engineering, 125(4), 195202. [ DOI:10.1061/(ASCE)0733950X(1999)125:4(195)] 11. Vimala, J., Latha, G., and Venkatesan, R.,(2014). Real time wave forecasting using artificial neural network with varying input parameter. 12. Dezvareh, R., (2019). Application of Soft Computing in the Design and Optimization of Tuned Liquid ColumnGas Damper for Use in Offshore Wind Turbines. International Journal of Coastal and Offshore Engineering, 2(4), pp.4757. 13. Dezvareh, R., (2019). Providing a new approach for estimation of wave setup in Iran coasts. Research in marine sciences, 4(1), 438448. 14. Dezvareh, R., Bargi, K. and Moradi, Y., (2012). Assessment of Wave Diffraction behind the Breakwater Using Mild Slope and Boussinesq Theories. International Journal of Computer Applications in Engineering Sciences, 2(2). 15. EtemadShahidi, A., and Mahjoobi, J., (2009), Comparison between M5′ model tree and neural networks for prediction of significant wave height in Lake Superior. Ocean Engineering, 36(1516), 11751181. [ DOI:10.1016/j.oceaneng.2009.08.008] 16. Samadi, M., Jabbari, E., and Azamathulla, H. M., (2014), Assessment of M5′ model tree and classification and regression trees for prediction of scour depth below free overfall spillways. Neural Computing and Applications, 24(2), 357366. [ DOI:10.1007/s0052101212309] 17. Kazeminezhad, M. H., EtemadShahidi, A., & Mousavi, S. J, (2005), Application of fuzzy inference system in the prediction of wave parameters. Ocean Engineering, 32(1415), 17091725. [ DOI:10.1016/j.oceaneng.2005.02.001] 18. Broomhead, D. S., and Lowe, D., (1988), Radial basis functions, multivariable functional interpolation and adaptive networks. Royal Signals and Radar Establishment Malvern (United Kingdom). 19. Powell, M. J. D., (1985), Radial basis function for multivariable interpolation: a review. In IMA Conference on Algorithms for the Approximation of Functions ans Data, 1985. RMCS. 20. Belloir, F., Fache, A., and Billat, A., (1999), A general approach to construct RBF netbased classifier. In ESANN (pp. 399404). Citeseer. 21. Li, Y., Qiang, S., Zhuang, X., and Kaynak, O., (2004), Robust and adaptive backstepping control for nonlinear systems using RBF neural networks. IEEE Transactions on Neural Networks, 15(3), 693701. [ DOI:10.1109/TNN.2004.826215] 22. Karayiannis, N. B., (1999), Reformulated radial basis neural networks trained by gradient descent. IEEE Transactions on Neural Networks, 10(3), 657671. [ DOI:10.1109/72.761725] 23. Chen, S., Wu, Y., and Luk, B. L., (1999), Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks. IEEE Transactions on Neural Networks, 10(5), 12391243. [ DOI:10.1109/72.788663] 24. Jang, J.S., (1993), ANFIS: adaptivenetworkbased fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665685. [ DOI:10.1109/21.256541] 25. Manoj, S. B. A., (2011), Identification and control of nonlinear systems using soft computing techniques. International Journal of Modeling and Optimization, 1(1), 24. [ DOI:10.7763/IJMO.2011.V1.5] 26. Pradhan, B., (2013), A comparative study on the predictive ability of the decision tree, support vector machine and neurofuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51, 350365. [ DOI:10.1016/j.cageo.2012.08.023] 27. Tushar, A., & Pillai, G. N., (2015), Extreme Learning ANFIS for classification problems. In Next Generation Computing Technologies (NGCT),1st International Conference on (pp. 784787). IEEE. [ DOI:10.1109/NGCT.2015.7375227] 28. Kaplan, K., Kuncan, M., and Ertunc, H. M., (2015), Prediction of bearing fault size by using model of adaptive neurofuzzy inference system. In Signal Processing and Communications Applications Conference (SIU), pp. 19251928. IEEE. [ DOI:10.1109/SIU.2015.7130237] 29. Jang, J.S. R., Sun, C.T., and Mizutani, E. (1997), Neurofuzzy and soft computing; a computational approach to learning and machine intelligence. [ DOI:10.1109/TAC.1997.633847] 30. Zamani, A., Solomatine, D., Azimian, A., and Heemink, A., (2008), Learning from data for windwave forecasting. Ocean Engineering, 35(10), 953962. [ DOI:10.1016/j.oceaneng.2008.03.007] 31. Kamranzad, B., EtemadShahidi, A., and Kazeminezhad, M. H.(2011), Wave height forecasting in Dayyer, the Persian Gulf. Ocean Engineering, 38(1), 248255. [ DOI:10.1016/j.oceaneng.2010.10.004] 32. Mafi, S., and Amirinia, G., (2017), Forecasting hurricane wave height in Gulf of Mexico using soft computing methods. Ocean Engineering, 146, 352362. [ DOI:10.1016/j.oceaneng.2017.10.003] 33. Deo, M. C., Jha, A., Chaphekar, A. S., and Ravikant, K. (2001). Neural networks for wave forecasting. Ocean Engineering, 28(7), 889898. [ DOI:10.1016/S00298018(00)000275]
