Journal of Tropical Oceanography >
The hydrodynamic characteristics prediction of landslide-induced surge waves based on non-hydrostatic model numerical simulation and convolutional neural network
Copy editor: YIN Bo
Received date: 2024-03-27
Revised date: 2024-05-30
Online published: 2024-06-13
Supported by
National Key Research and Development Program of China(2022YFC3103601)
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.
WANG Aoyu , QU Ke , WANG Xu , GAO Rongze , MEN Jia . The hydrodynamic characteristics prediction of landslide-induced surge waves based on non-hydrostatic model numerical simulation and convolutional neural network[J]. Journal of Tropical Oceanography, 2025 , 44(2) : 187 -195 . DOI: 10.11978/2024071
图1 数值验证计算区域布置图a. 侧视图; b. 俯视图。G1和G2(3)为测点1和测点2(3)。图a中半椭圆表示滑块侧面的形状; 图b中的椭圆表示滑块俯视的形状 Fig. 1 Computational domain for numerical validation a. Side view; b. Top view. G1 and G2 (3) are measurement points 1 and 2 (3). The semi ellipse in Figure A represents the shape of the side of the slider; The ellipse in Figure B represents the shape of the slider when viewed from above |
图2 测点1 (a)、测点2 (b)、测点3 (c)数值模拟结果验证图图中横纵坐标均进行了无量纲化处理, 纵坐标标目为表面高程(η)/滑块宽度(B), 横坐标标目为时间(t)/式(10)中的t0 Fig. 2 Validation of numerical simulation results for monitoring point1(a), point2(b) and point3(c) The horizontal and vertical coordinates in the figure have been dimensionless, with the vertical axis marked as surface elevation (η)/slider width (B), the horizontal axis is time (t) divided by t0 in equation (10) |
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