Coastal Wave Height Prediction using Recurrent Neural Networks (RNNs) in the South Caspian Sea

References

• Alizadeh, M. J., H. Shahheydari, M. R. Kavianpour, H. Shamloo, and R. Barati. 2017. Prediction of longitudinal dispersion coefficient in natural rivers using a cluster-based Bayesian network. Environmental Earth Sciences 76(2):86.
   Web of Science ®Google Scholar
• Agarwal, J., and M. C. Deo. 2002. On-line wave prediction. Marine Structures 15(1):57–74.
   Web of Science ®Google Scholar
• Avakiand, A. B., and V. Shirokov. 1993. Rational Use and Protection of Water Resource. Yekaterinburg: Publ. House Victor (in Russian).
   Google Scholar
• Asgari, H., X. Q. Chen, M. Morini, M. Pinelli, R. Sainudiin, and P. R. Spina. 2016. NARX models for simulation of the start-up operation of a single shaft gas turbine. Applied Thermal Engineering 93:368*–376.
   Web of Science ®Google Scholar
• Ashton, A. D., and A. B. Murray. 2006. High-angle wave instability and emergent shoreline shapes: 1. modeling of sand waves, flying spits, and capes. Journal of Geophysics (F04):F04011. doi: 10.1029/2005JF000422
   Web of Science ®Google Scholar
• Barati, R. 2011. Parameter estimation of nonlinear Muskingum models using Nelder-Mead simplex algorithm. Journal of Hydrologic Engineering 16(11):946–954.
   Web of Science ®Google Scholar
• Barati, R. 2013. Application of excel solver for parameter estimation of the nonlinear Muskingum models. KSCE Journal of Civil Engineering 17(5):1139–1148.
   Web of Science ®Google Scholar
• Barati, R., S. A. A. S. Neyshabouri, and G. Ahmadi. 2014 a. Development of empirical models with high accuracy for estimation of drag coefficient of flow around a smooth sphere: An evolutionary approach. Powder Technology 257:11–19.
   Web of Science ®Google Scholar
• Barati, R., S. S. Neyshabouri, and G. Ahmadi. 2014b. Sphere drag revisited using shuffled complex evolution algorithm. In River Flow.
   Google Scholar
• Blaker, R. S., and J. P. Norton. 2007. Efficient investigation of the feasible parameter set for large mode. Proceedings of the International Congress on Modeling and Simulation, Christchurch, New Zealand, 1526–1532.
   Google Scholar
• Booij, N., Ris, R. C., and Holthuijsen, L. H. 1999. A third-generation wave model for coastal regions. 1. Model description and validation. J. Geophys. Res. 104 (C4), 7649–7666. doi:10.1029/98JC02622.
   Web of Science ®Google Scholar
• Cunha, C., and Soares, C. G. 1999. On the choice of data transformation for modelling time series of significant wave height. Ocean Eng. 26(6), 489–506.
   Web of Science ®Google Scholar
• Camenen, B., and M. Larson. 2007. Predictive formulas for breaker depth index and breaker type. Journal of Coastal Research 23(4):1028–1041.
   Web of Science ®Google Scholar
• Deo, M. C., and C. S. Naidu. 1999. Real time wave forecasting using neural networks. Ocean Engineering 26:191–203.
   Web of Science ®Google Scholar
• Deo, M., C. Jha, A. S. Chaphekar, and K. Ravikant. 2001. Neural networks for wave forecasting. Ocean Engineering 28:889–898.
   Web of Science ®Google Scholar
• Duan, W. Y., Y. Han, L. M. Huang, B. B. Zhao, and M. H. Wang. 2016. A hybrid EMD-SVR model for the short-term prediction of significant wave height. Ocean Engineering 124:54–73.
   Web of Science ®Google Scholar
• Golshani, A., A. Nakhaee, V. Chegini, and M. Jandaghi Alaee. 2005. Wave hindcast study of the Caspian Sea. Journal of Marine Engineering, Iranian Association of Naval Architecture & Marine Engineering 1(2):19–25.
   Google Scholar
• Group The WAMDI. (1988). The WAM model-A third generation ocean wave prediction model. Journal of Physical Oceanography 18, 1775–1810.
   Web of Science ®Google Scholar
• Haykin, S. 1999. Neural Networks: A Comprehensive Foundation. NJ: Prentice-Hall, 842.
   Google Scholar
• Ho, S. L., M. Xie, and T. N. Goh. 2002. A comparative study of neural network and Box-Jenkins ARIMA modeling in the time series prediction. Computers and Industrial Engineering 42(2–4):371–375.
   Web of Science ®Google Scholar
• Hosseini, K., E. J. Nodoushan, R. Barati, and H. Shahheydari. 2016. Optimal design of labyrinth spillways using meta-heuristic algorithms. KSCE Journal of Civil Engineering 20(1):468–477.
   Web of Science ®Google Scholar
• Huo, F., and A. N. Poo. 2013. Nonlinear autoregressive network with exogenous inputs based contour error reductionin CNC machines. International Journal of Machine Tools & Manufacture 67:45–52.
   Web of Science ®Google Scholar
• Ibrahim, M., S. Jemei, G. Wimmer, and D. Hissel. 2016. Nonlinear autoregressive neural network in an energy management strategy for battery/ultra-capacitor hybrid electrical vehicles. Electric Power System 136:262–269.
   Web of Science ®Google Scholar
• Jane, R., Dalla Valle, L., Simmonds, D., and Raby, A. (2016). A copula-based approach for the estimation of wave height records through spatial correlation. Coastal Engineering 117, 1–18.
   Web of Science ®Google Scholar
• Kermanshahi, B. 1998. Recurrent neural network for forecasting next 10 years loads of nine Japanese utilities. Neuro Computing 23(1–3):125–133.
   Web of Science ®Google Scholar
• Krishna Kumar, N., R. Savitha, and A. Al Mamun. 2017. Regional ocean waves height prediction using sequential learning neural networks. Ocean Engineering 129:605–612.
   Web of Science ®Google Scholar
• Lin, T., B. G. Home, and C. L. Giles. 1998. How embedded memory in recurrent neural network architectures helps learning long-term temporal dependencies. Neural Networks 11:861–868.
   PubMed Web of Science ®Google Scholar
• Londhe, S. N., and M. C. Deo. 2004. Artificial neural networks for wave propagation. Journal of Coastal Research 20(4):1061–1069.
   Web of Science ®Google Scholar
• López, M. 2012. Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study. Electric Power System 91:18–27.
   Web of Science ®Google Scholar
• López-Ruiz, A., M. Ortega-Sánchez, A. Baquerizo, and M. A. Losada. 2012. Short and medium-term evolution of shoreline undulations on curvilinear coasts. Geomorphology 159–160:189–200. doi: 10.1016/j.geomorph.2012.03.026
   Web of Science ®Google Scholar
• López-Ruiz, A., M. Ortega-Sánchez, A. Baquerizo, and M. A. Losada. 2014. A note on along- shore sediment transport on weakly curvilinear coasts and its implications. Coastal Engineering 88:143–153. doi: 10.1016/j.coastaleng.2014.03.001
   Web of Science ®Google Scholar
• Mamedov, R., and H. Khoshravan. 2015. The Atlas of Caspian Sea. Hydromorphology. Sophia Publishing Group Inc., 225.
   Google Scholar
• Mandal, S., and N. Prabaharan. 2006. Ocean wave forecasting using recurrent neural networks. Ocean Engineering 33(10):1401–1410.
   Web of Science ®Google Scholar
• Makarynskyy, O. 2004. Improving wave predictions with artificial neural networks. Ocean Engineering 31(5):709–724.
   Web of Science ®Google Scholar
• McCulloch, W. S., and Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133.
   Google Scholar
• Moeini, M. H., A. Etemad-Shahidi, and V. Chegini. 2010. Wave modeling and extreme value analysis off the northern coast of the Persian Gulf. Applied Ocean Research 32:209–218.
   Web of Science ®Google Scholar
• More, A., and M. C. Deo. 2003. Forecasting wind with neural networks. Marine Structures 16(1):35–39.
   Web of Science ®Google Scholar
• Nyanteh, Y. D., S. K. Srivastava, C. S. Edrington, and D. A. Cartes. 2013. Application of artificial intelligence to stator winding fault diagnosis in permanent magnet synchronous machines. Electric Power System 103:201–213.
   Web of Science ®Google Scholar
• Principe, J. C., N. R. Euliano, and W. C. Lefebvre. 2000. Neural and Adaptive Systems: Fundamentals through Simulation. John Wiley and Sons, 672.
   Google Scholar
• Robertson, B., B. Gharabaghi, and K. Hall. 2015. Prediction of incipient breaking wave-heights using artificial neural networks and empirical relationships. Coastal Engineering Journal 57(4):1550018. ( 1–27). doi: 10.1142/S0578563415500187
   Web of Science ®Google Scholar
• Soares, C., Ferreira, A., and Cunha, C. 1996. Linear models of the time series of significant wave height on the Southwest Coast of Portugal. Coast. Eng. 29(1-2), 149–167.
   Web of Science ®Google Scholar
• Splinter, K. D., M. A. Davidson, A. Golshani, and R. Tomlinson. 2012. Climate controls on longshore sediment transport. Continental Shelf Research 48:146–156. doi: 10.1016/j.csr.2012.07.018
   Web of Science ®Google Scholar
• Subba, R., S. Mandal, and N. Prabaharan. 2001. Wave forecasting in near real time basis by neural network. Proc. International Conference in Ocean Engineering, IIT Madras Chennai, India, 105–108.
   Google Scholar
• Subba, R., and S. Mandal. 2005. Hindcasting of storm waves using neural network. Ocean Engineering 32(5–6):667–684.
   Web of Science ®Google Scholar
• Riedmiller, M. 1994. Advanced supervised learning in multi-layer perceptron's – from back propagation to adaptive learning algorithms. Computers Standards & Interfaces 16:265–278.
   Web of Science ®Google Scholar
• Tsai, C., Chen, H., You, S., 2009. Toe scour of seawall on a steep seabed by breaking waves. J. Waterway Port Coast. Ocean Eng. 135(2), 61–68.
   Web of Science ®Google Scholar
• Tveter, D. R. 2000. Back Propagator's Review. http://www.dontveter.com/bpr/bpr.html
   Google Scholar
• Zanuttigh, B., S. M. Formentin, and J. W. van der Meer. 2016. Prediction of extreme and tolerable wave overtopping discharges through an advanced neural network. Ocean Engineering 127:7–22.
   Web of Science ®Google Scholar
• Zhang, W., R. Schneider, and J. Harff. 2012. A multi-scale hybrid long-term morphodynamic model for wave-dominated coasts. Geomorphology 1–13:149–150. doi: 10.1016/j.geomorph.2012.01.019
   Web of Science ®Google Scholar