一种用于时变波浪爬高预测的物理信息机器学习模型
A Physics-informed machine learning model for time-dependent wave runup prediction
AI总结：
本文提出一种物理信息机器学习方法，利用条件生成对抗网络将XBeach模型Surfbeat模式生成的波浪爬高时频图像映射为非静压模式的高保真图像，再通过逆小波变换重建时变波浪爬高，实现高效且准确的预测。
摘要：
波浪爬高是影响海岸洪水、岸线变化和海岸结构物破坏的关键因素。气候变化预计还会加剧波浪爬高对沿海地区的影响。因此，快速而准确地估计波浪爬高对于有效的海岸工程设计和管理至关重要。然而，由于该过程固有的非线性和非平稳性，即使使用最先进的机器学习技术，预测时变波浪爬高仍然具有挑战性。本研究提出了一种基于物理信息机器学习的方法，以高效、准确地模拟时间序列波浪爬高。该方法结合了XBeach模型中Surfbeat（XBSB）模式的计算效率和非静压（XBNH）模式的精度。具体而言，使用条件生成对抗网络（cGAN）将XBSB的波浪爬高图像表示映射到XBNH的相应图像。这些图像的生成方式是：首先将波浪爬高信号转换为时频尺度图，然后将其转化为图像表示。cGAN模型通过引入来自XBSB的基于物理的知识，在图像到图像的映射任务中取得了更好的性能。模型训练完成后，可以预测基于XBNH的高保真尺度图，然后利用逆小波变换重建时间序列波浪爬高。模拟结果强调了所提模型在预测波浪爬高方面的效率和鲁棒性，表明其在风险评估和管理应用中具有潜在价值。
英文摘要：
Wave runup is a critical factor affecting coastal flooding, shoreline changes, and damage to coastal structures. Climate change is also expected to amplify wave runup's impact on coastal areas. Therefore, fast and accurate wave runup estimation is essential for effective coastal engineering design and management. However, predicting the time-dependent wave runup is challenging due to the intrinsic nonlinearities and non-stationarity of the process, even with the use of the most advanced machine learning techniques. In this study, a physics-informed machine learning-based approach is proposed to efficiently and accurately simulate time-series wave runup. The methodology combines the computational efficiency of the Surfbeat (XBSB) mode with the accuracy of the nonhydrostatic (XBNH) mode of the XBeach model. Specifically, a conditional generative adversarial network (cGAN) is used to map the image representation of wave runup from XBSB to the corresponding image from XBNH. These images are generated by first converting wave runup signals into time-frequency scalograms and then transforming them into image representations. The cGAN model achieves improved performance in image-to-image mapping tasks by incorporating physics-based knowledge from XBSB. After training the model, the high-fidelity XBNH-based scalograms can be predicted, which are then employed to reconstruct the time-series wave runup using the inverse wavelet transform. The simulation results underscore the efficiency and robustness of the proposed model in predicting wave runup, suggesting its potential value for applications in risk assessment and management.
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