Assessment and prediction of significant wave height using hybrid CNN-BiLSTM deep learning model for sustainable wave energy in Australia

Abstract

Wave energy is regarded as one of the powerful renewable energy sources and depends on the assessment of significant wave height (Hs) for feasibility. Hence, this study explores the potential of wave energy by assessing and predicting Hs for two study sites in Queensland (Emu Park and Townsville), Australia. Assessment and prediction of Hs is extremely important for reliable planning, cost management and implementation of wave energy projects. The study utilized oceanic datasets based on wave measurements obtained from buoys along coastal regions of Queensland that are transmitted to nearby receiver stations. The parameters of the datasets include maximum wave height, zero up crossing wave period, peak energy wave period and sea surface temperature to accurately predict Hs. A new hybrid Convolutional Neural Network (CNN) and Bidirectional Long Short Term (BiLSTM) deep learning model with Multivariate Variational Mode Decomposition (MVMD) is developed which is benchmarked by Multi-Layer Perceptron (MLP), Random Forest (RF) and Categorical Boosting (CatBoost) to compare the performance. All models attain relatively high-performance results. The MVMD-CNN-BiLSTM attains slightly better performance values for both study sites among all developed models with highest correlation values of 0.9957 and 0.9986 for Emu Park and Townsville, respectively. Other performance evaluation metrics were also higher for MVMD-CNN-BiLSTM with lowest error values in comparison to the benchmark models. The annual mean of Hs is also computed to compare and obtain an insight with a linear projection. There is a greater ocean wave energy potential for Emu Park for a 10-year period with a projected mean Hs of 0.865 m in comparison to Townsville where the projected mean was of 0.665 m.