Prediction of Coastal Wave Height using Hybrid-Wavelet Methods of Artificial Intelligence (Case study: Amirabad Port of the Caspian Sea)

 Todays, considering climate change and its impact on the state of sea waves and the dangers caused by its severity, assessing and estimating the height of the significant wave in the seas is of great importance. Predicting the height of the significant wave in Amirabad port by using a combination of variables representing the characteristics of waves and meteorology, developing artificial intelligence models and de-noising the data using wavelet theory, and finally extracting the mathematical relationships governing the principles of marine-meteorological engineering to estimate altitude Wave is one of the unique goals and innovations in this study.

In this study, wave height in the Caspian Sea port of Amirabad, using single and hybrid-wavelet artificial intelligence methods, including Artificial Neural Network (ANN, WANN), multilayer perceptron with the Levenberg-Margaret training algorithm, Adaptive Fuzzy-neural Inference System (ANFIS, WANFIS), and Gene Expression Programming (GEP, WGEP) in different short time lags including no time lag, 3 and 6 hour time lags is estimated. For this purpose, hourly waves and meteorological data were used in 2018.

The results indicate that noise removed by wavelet analysis can improve performance in all models. Also, in this study, hybrid-wavelet models have presented better results than single models. Among all the models, the WGEP model was the best model and the ANN model was the weakest model for all time steps. The highest values of correlation coefficient and Nash-Sutcliffe efficiency coefficient are related to no time lags and WGEP model and its values are 0.960 and 0.980 and root of the mean squared error and the mean absolute value of the error values are 0.037 and 0.078 meters, respectively. The lowest values of correlation coefficient and Nash-Sutcliffe efficiency coefficient and the highest values of RMSE and MAE are related to the single ANN model for 6 hours lags with the values 0.509, 0.607, 0.181, and 0.286.

The results of their three single and hybrid-wavelet methods can be acceptable for estimating the significant wave height in Amirabad port. Also, disruption of observational data reduces many measurement errors and increases the performance of artificial intelligence models. This study has a significant impact on crisis and coastline management and can be a strategic model for managers, policymakers, and researchers for future research.