A Recurrent-Cascade-Neural network- nonlinear autoregressive networks with exogenous inputs (NARX) approach for long-term time-series prediction of wave height based on wave characteristics measurements

Abstract

To overcome the data limitations of wave and environmental characteristics for estimating the significant wave height (Hs), this study investigates the use of an available dataset for wave characteristics to predict a long-term period of Hs for the Red Sea. A novel soft-computing approach is designed for the time-series prediction of Hs using the time delay of average wave period and Hs measurements. Buoy station of Jeddah, Saudi Arabia, datasets for 2009 and 2010 are used for designing and validating the proposed model. The time-series identification system of nonlinear autoregressive networks with exogenous inputs (NARX) and recurrent neural network (RNN) with cascade input variables based on neural network solutions were integrated through a sequential system to develop the new approach RNNARX. The RNNARX was compared with the conventional RNN, NARX neural network (NNARX), and previous studies’ results to verify its robustness. The proposed model was evaluated to predict hourly and daily Hs. The results showed that the proposed model achieves good performance for long-term prediction of Hs. The sequential system improved the performance of RNN and NNARX models for the time-series prediction of Hs. The comparison results showed that RNNARX performance is suitable for the Hs prediction of the study region. The model performances in terms of coefficient determination and root mean square error were 0.90 and 0.149 m, respectively, with a prediction error of 2.95% for four days lead times.