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.

中文翻译：

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通过深度学习提高波高预测精度

提出了一种新颖的卷积神经网络-长短期记忆（CNN-LSTM）模型用于波高预测。该模型有效提取了风速、风向、波高、纬度、经度等相关特征。所提出的模型优于多层感知器（MLP）、支持向量机（SVM）、随机森林和 LSTM 等传统机器学习算法，特别是对于极值和波动。该模型对于 MLP、SVM、随机森林和 LSTM 的平均均方根误差 (RMSE) 显着降低，分别为 71.1%、72.8%、71.9% 和 72.2%。我们的模型在计算上比传统的数值模拟更高效，使其适合实时应用。此外，与传统模型相比，它具有更好的长期稳健性。 CNN 和 LSTM 技术的集成提高了波高预测精度，同时增强了其效率和鲁棒性。所提出的 CNN-LSTM 模型为有效的波高预测提供了一种有前途的工具，为沿海防灾减灾做出了宝贵的贡献。未来的研究应该以提高长期预测精度为目标，我们相信CNN-LSTM模型在制定实时沿海防灾减灾措施方面发挥着至关重要的作用。总的来说，我们的研究代表了使用机器学习技术实现更准确、更高效的波高预测的重要一步。