基于非静压模型数值模拟与卷积神经网络的滑坡涌浪水动力特性预测  

The hydrodynamic characteristics prediction of landslide-induced surge waves based on non-hydrostatic model numerical simulation and convolutional neural network

作　　者：王傲宇 屈科 王旭[1] 高榕泽 门佳 WANG Aoyu;QU Ke;WANG Xu;GAO Rongze;MEN Jia(School of Hydraulic and Environmental Engineering,Changsha University of Science&Technology,Changsha 410114,China;Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province,Changsha 410114,China;Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province,Changsha 410114,China)

机构地区：[1]长沙理工大学水利与环境工程学院,湖南长沙410114 [2]洞庭湖水环境治理与生态修复湖南省重点实验室,湖南长沙410114 [3]水沙科学与水灾害防治湖南省重点实验室,湖南长沙410114

出　　处：《热带海洋学报》2025年第2期187-195,共9页Journal of Tropical Oceanography

基　　金：国家重点研发计划课题项目(2022YFC3103601)。

摘　　要：海底滑坡作为一种破坏力巨大并且在全世界范围广泛分布的自然灾害,往往会给人类的生命安全产生巨大的威胁。滑坡产生的巨大涌浪会对海洋建筑物造成严重破坏,因此迅速预测和评估海底滑坡所能产生的涌浪大小是防灾减灾工作的关键部分,对海洋资源的开发利用以及人民生命财产安全至关重要。文章以非静压模型(non-hydrostatic wave model,NHWAVE)进行了滑坡涌浪的数值模拟,得到了不同滑坡产生涌浪的数据,并以一维卷积神经网络(1-dimensional convolutional neural network,CONV1D)为基础,训练了滑坡产生涌浪的预测模型。该模型使用了不同测点和不同类型的滑坡数据集进行训练,并使用平均绝对误差等评价指标对卷积神经网络的预测结果进行评估。在使用少量数据集的条件下,卷积神经网络能很好地学习到滑坡涌浪的规律,并且对于数据集中不存在的特征也能预测得到不错的结果,具有较好的泛化能力。模型训练好之后,只要实时输入滑坡发生位置自由表面的水位数据,神经网络就能在短时间内预测出未来下游测点涌浪的时程曲线。通过神经网络预测,可以提前对灾害进行评估,从而采取及时有效的应对措施。Submarine landslides,as a natural disaster with immense destructive potential and widespread distribution worldwide,often pose significant threats to the safety of human lives.The massive waves generated by these landslides can cause severe damage to marine structures.Therefore,rapidly predicting and assessing the size of the waves generated by underwater landslides is a crucial part of disaster prevention and mitigation efforts,essential for the development and utilization of marine resources,as well as for the safety of human lives and properties.In this study,numerical simulations of landslide-generated waves were conducted using the non-hydrostatic wave model NHWAVE(a non-hydrostatic wave model) to obtain wave data for different types of landslides.Subsequently,a CONV1D(1-dimensional convolutional neural network) was trained as the prediction model for landslide-generated waves.The model was trained using datasets for various monitoring points and different types of landslides,and evaluation metrics such as mean squared error were employed to assess the prediction performance of the convolutional neural network.The results indicate that,under the condition of using a limited amount of data,the convolutional neural network can effectively learn the patterns of landslide-generated waves.Moreover,it can predict reasonably well even for features that are not uniquely present in the dataset,demonstrating good generalization capability.Once the model is trained,inputting real-time water level data from the location of the landslide occurrence enables the neural network to predict the temporal wave profiles at downstream monitoring points in a short time.By using neural networks for prediction,it is possible to assess disasters in advance and take timely and effective response measures.

关 键 词：海底滑坡 卷积神经网络 时序预测 灾害预警 

分 类 号：P751[交通运输工程—港口、海岸及近海工程]