Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave energy.Deep learning methods such as recurrent and convolutional neural networks have achieved good results in S...Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave energy.Deep learning methods such as recurrent and convolutional neural networks have achieved good results in SWH forecasting.However,these methods do not adapt well to dynamic seasonal variations in wave data.In this study,we propose a novel method—the spatiotemporal dynamic graph(STDG)neural network.This method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic fusion.First,considering the dynamic seasonal variations in the wave direction over time,the network models wave dynamic spatial dependencies from long-and short-term pattern perspectives.Second,to correlate multiple characteristics with SWH,the network introduces a cross-characteristic transformer to effectively fuse multiple characteristics.Finally,we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three categories.The experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value prediction.Furthermore,an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.展开更多
Wave height forecast(WHF)is of great significance to exploit the marine renewables and improve the safety of ship navigation at sea.With the development of machine learning technology,WHF can be realized in an easy-to...Wave height forecast(WHF)is of great significance to exploit the marine renewables and improve the safety of ship navigation at sea.With the development of machine learning technology,WHF can be realized in an easy-to-operate and reliable way,which improves its engineering practicability.This paper utilizes a data-driven method,Gaussian process regression(GPR),to model and predict the wave height on the basis of the input and output data.With the help of Bayes inference,the prediction results contain the uncertainty quantification naturally.The comparative studies are carried out to evaluate the performance of GPR based on the simulation data generated by high-order spectral method and the experimental data collected in the deep-water towing tank at the Shanghai Ship and Shipping Research Institute.The results demonstrate that GPR is able to model and predict the wave height with acceptable accuracy,making it a potential choice for engineering application.展开更多
基金The National Key R&D Program of China under contract No.2021YFC3101604。
文摘Accurate significant wave height(SWH)prediction is essential for the development and utilization of wave energy.Deep learning methods such as recurrent and convolutional neural networks have achieved good results in SWH forecasting.However,these methods do not adapt well to dynamic seasonal variations in wave data.In this study,we propose a novel method—the spatiotemporal dynamic graph(STDG)neural network.This method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic fusion.First,considering the dynamic seasonal variations in the wave direction over time,the network models wave dynamic spatial dependencies from long-and short-term pattern perspectives.Second,to correlate multiple characteristics with SWH,the network introduces a cross-characteristic transformer to effectively fuse multiple characteristics.Finally,we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three categories.The experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value prediction.Furthermore,an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.
基金supported by the National Natural Science Foundation of China(Grant No.52211530101)the YEQISUN Joint Funds of the National Natural Science Foundation of China(Grant No.U2141228).
文摘Wave height forecast(WHF)is of great significance to exploit the marine renewables and improve the safety of ship navigation at sea.With the development of machine learning technology,WHF can be realized in an easy-to-operate and reliable way,which improves its engineering practicability.This paper utilizes a data-driven method,Gaussian process regression(GPR),to model and predict the wave height on the basis of the input and output data.With the help of Bayes inference,the prediction results contain the uncertainty quantification naturally.The comparative studies are carried out to evaluate the performance of GPR based on the simulation data generated by high-order spectral method and the experimental data collected in the deep-water towing tank at the Shanghai Ship and Shipping Research Institute.The results demonstrate that GPR is able to model and predict the wave height with acceptable accuracy,making it a potential choice for engineering application.
