# A Hybrid Model of Conformer and LSTM for Ocean Wave Height Prediction
## 4 Conclusions  
This study proposes a hybrid model (Conformer-LSTM) based on Conformer and LSTM for wave height prediction. By introducing the self-attention mechanism and convolutional layer from Conformer combined with LSTM's processing of long-term dependencies in time series data, the model shows excellent performance in capturing non-linear features and dynamic changes in time series data. At the same time, the use of an adaptive feature fusion weight network enhances the model's recognition and utilization efficiency of key features. Experimental data come from the National Oceanic and Atmospheric Administration (NOAA) NDBC buoy data, and evaluation indicators include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R\({}^{2}\)). The experimental results show that the Conformer-LSTM model performs better than traditional LSTM, CNN, and CNN-LSTM models at multiple stations, confirming its potential in wave height prediction. The CNN-LSTM model proposed by [PERSON] (2018) showed better performance compared to standalone LSTM and random forest models. Despite these improvements, our Conformer-LSTM model demonstrated a higher accuracy in both short-term and medium-term wave height predictions, as evidenced by lower MAPE and higher R\({}^{2}\) values. Through comparative analysis of the prediction effects of different models, it can be observed that the Conformer-LSTM model has a significant advantage in capturing complex time series features, especially in dealing with nonlinear and non-stationary wave data, showing higher prediction accuracy and robustness. Further verification of the prediction effects at more stations demonstrates the model's generalization ability and practicality. The implications of accurate wave height prediction extend beyond maritime safety. Reliable forecasts contribute to the economy by optimizing shipping routes, reducing fuel consumption, and minimizing the risk of maritime accidents. This, in turn, can lower insurance costs and enhance the overall efficiency of maritime logistics. Furthermore, precise wave height predictions support coastal zone management and the planning of offshore operations, contributing to the sustainable development of marine resources and the protection of coastal infrastructure. In terms of societal impact, improved wave height prediction models can enhance disaster preparedness and response. Early warnings of extreme wave events can save lives and reduce property damage in coastal communities. Additionally, accurate wave forecasts support recreational activities and tourism, ensuring safety and enhancing the experience for those engaging in water sports and coastal tourism. However, this study also has some limitations. This model is specifically designed to handle non-linear, medium- to long-period data, making it particularly suitable for scenarios where such characteristics are prominent. However, the model may not perform as well with data that are primarily linear or exhibit very short-term periodicity. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Network** & **MAE** & **RMSE** & **MAPE (\%)** & **\(\mathbf{R^{2}}\)** \\ \hline LSTM & 10.82\% & 8.78\% & 10.54\% & 0.95\% \\ CNN & 15.02\% & 15.56\% & 15.58\% & 1.35\% \\ CNN-LSTM & 5.13\% & 7.02\% & 6.25\% & 0.58\% \\ BiLSTM & 11.65\% & 12.65\% & 8.71\% & 1.05\% \\ TCN & 19.89\% & 20.0.\% & 17.81\% & 2.24\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Accuracy improvement of Conformer-LSTM compared to other networks. Another limitation of this study is the small number of buoys used for data collection. Future research should aim to include a greater number of buoys, potentially in diverse geographical locations, to validate and extend the applicability of the proposed method. Focusing on a single location with a more significant number of data points could provide additional validation and improve the generalizability of the results.