Improving wave height prediction accuracy with deep learning

Abstract

A novel convolutional neural network-long short-term memory (CNN-LSTM) model is proposed for wave height prediction. The model effectively extracts relevant features such as wind speed, wind direction, wave height, latitude, and longitude. The proposed model outperforms traditional machine learning algorithms such as multi-layer perceptron (MLP), support vector machine (SVM), random forest and LSTM, especially for extreme values and fluctuations. The model has a significantly lower average root mean square error (RMSE) of 71.1%, 72.8%, 71.9% and 72.2% for MLP, SVM, random forest and LSTM, respectively. Our model is computationally more efficient than traditional numerical simulations, making it suitable for real-time applications. Moreover, it has better long-term robustness compared to traditional models. The integration of CNN and LSTM techniques improves wave height prediction accuracy while enhancing its efficiency and robustness. The proposed CNN-LSTM model provides a promising tool for effective wave height prediction, making a valuable contribution to coastal disaster prevention and mitigation. Future research should aim to improve long-term prediction accuracy, and we believe that the CNN-LSTM model plays a crucial role in developing real-time coastal disaster prevention and mitigation measures. Overall, our study represents a significant step towards achieving more accurate and efficient wave height prediction using machine learning techniques.