# A Hybrid Model of Conformer and LSTM for Ocean Wave Height Prediction
###### Abstract  
This study proposes a hybrid model (Conformer-LSTM) based on Conformer and Long Short-Term Memory networks (LSTM) to overcome the limitations of existing techniques and enhance the accuracy and generalizability of wave height predictions. The model combines the advantages of self-attention mechanisms and convolutional neural networks. It captures global dependencies through multi-head self-attention and utilizes convolutional layers to extract local features, thereby enhancing the model's adaptability to dynamic changes in time series. The LSTM component handles long-term dependencies, optimizing the coherence and stability of predictions. Additionally, an adaptive feature fusion weight network is introduced to further improve the model's recognition and utilization efficiency of key features. Experimental data come from the National Oceanic and Atmospheric Administration buoy data, covering wave height, wind speed, and other data from key maritime areas. Evaluation metrics include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R\({}^{2}\)), ensuring a comprehensive assessment of model performance. The results show that the Conformer-LSTM model outperforms traditional LSTM, CNN, and CNN-LSTM models at multiple sites, confirming its potential in wave height prediction. wave height prediction; conformer; deep learning; time series forecasting +
Footnote †: journal: Medical Informatics and Systems  
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Footnote 1: email: xiao.j.xu@u-cse.u-cse. Nonlinearity means that data relationships cannot be captured by simple linear models, and non-stationarity means that the statistical properties of data change over time. In addition, the impact weight of multi-dimensional features on the prediction target is also very important, and data are easily affected by sudden events, introducing outliers or mutations. Addressing these anomalies and adapting the model to dynamic changes are important issues for accurate one-step prediction [4]. Current one-step prediction methods mainly include numerical simulation, statistical analysis, and deep-learning techniques. Numerical simulation methods simulate the dynamics of waves through global and regional wave models. These models are based on physical equations and consider the impact of wind fields, currents, and atmospheric pressure to simulate wave generation, propagation, and decay. Global wave models, such as WAVEWATCH III (WW3), provide large-scale wave forecasts [5], while regional wave models, such as SWAN (Simulating Waves Nearshore) [6], offer higher resolution forecasts for specific maritime areas. [PERSON] et al. [7] have evaluated the performance of different source term packages in WW3 wave models under various wave conditions in the Indian Ocean. [PERSON], [PERSON], [PERSON], et al. [8] have used the SWAN wave model in the Bohai Sea, Yellow Sea, and East China Sea, evaluating the performance of seven wind resources in simulating wave height. These methods are more accurate in predicting large-scale fluctuations but have high computational costs and a high dependency on the quality and real-time updates of input data. Statistical methods predict wave heights by analyzing the relationship between historical meteorological and wave data. By establishing regression models or using time series analysis techniques, such as the ARIMA (Autoregressive Integrated Moving Average model) [9] and SARIMA (Seasonal Autoregressive Integrated Moving Average) [2] models, statistical methods can effectively predict wave variations. [PERSON], [PERSON], [PERSON], et al. [10], using the SARIMA method, have found that there is a rich wave energy resource in the waters around the Taiwan Strait, the Luzon Strait, and the northern South China Sea, with a significant increasing trend. The advantages of these methods lie in their high computational efficiency, but they may be limited by the availability and quality of historical data.