卷积神经网络方法在岛礁类海啸波水动力特性演变的应用

doi:10.11978/2023111

Abstract

Abstract:

Tsunami is a serious marine disaster, and accurate tsunami prediction is of great significance to marine engineering and the safety of people’s lives and property. In this paper, based on 1-dimensional convolutional neural network (CONV1D), the evolution model of tsunami-like hydrodynamic characteristics of reef topography is constructed. By inputting observed values of wave heights resembling tsunami waves, the water inundation time series curves for specified locations on islands and reefs are generated. This achieves a prediction from one time series to another, serving the purpose of marine disaster prevention. The results indicate that the average relative error in predicting the arrival time of tsunami-like waves is 0.71%, and the average relative error in predicting maximum water levels is 6.99%. The hydrodynamic characteristics of island and reef terrains resembling tsunami waves obtained through CONV1D exhibit a strong alignment with numerical results.

Key words: deep learning, convolutional neural network, tsunami prediction, hydrodynamic characteristics, times series

GAO Rongze, QU Ke, REN Xingyue, WANG Xu. Application of convolutional neural network methods in the evolution of hydrodynamic characteristics of tsunamis like-wave over fringing reef[J].Journal of Tropical Oceanography, 2024, 43(4): 68-75.

Figures/Tables 9

Fig. 1

Fig. 2

Fig. 3

Tab. 1

Fig. 4

Fig. 5

Fig. 6

Tab. 2

Maximum water level and arrival time of different wave heights under a 6-second observation duration"

H/m	$η ob$ /m	$η p$ /m	$E η$ /%	/s	$t p$ /s	$E t$ /%
0.25	0.0550	0.0527	4.12	13.20	13.40	1.515
0.30	0.0616	0.0592	4.02	12.4	12.6	1.612
0.35	0.0706	0.0629	10.91	11.7	11.9	1.709
0.40	0.0878	0.0751	14.46	11.2	11.3	0.892
0.45	0.0843	0.7066	9.13	10.8	10.8	0
0.50	0.0872	0.0836	4.12	10.4	10.4	0
0.55	0.0897	0.0849	5.35	9.9	10.0	1.010
0.60	0.1018	0.0884	12.34	9.5	9.6	0.105
0.65	0.1046	0.1016	2.86	8.9	8.9	0
0.70	0.1049	0.0991	2.64	8.8	8.8	0
0.75	0.1025	0.0964	5.85	8.6	8.6	0

Tab. 2

Fig. 7

References 22

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观测时长/s	参数量	预测所用时间/s
5	161110	0.0164
6	186710	0.0175
7	212310	0.0182
8	237910	0.0201
9	263510	0.0175

Application of convolutional neural network methods in the evolution of hydrodynamic characteristics of tsunamis like-wave over fringing reef

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