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

Краткое изложение: Rapid and accurate tsunami is an important part of Marine disaster prevention work, which is of great significance to Marine engineering and people's life and property safety. 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 error in predicting the arrival time of tsunami-like waves is 0.71%, and the average 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.