Neural network models for seabed stability: a deep learning approach to wave-induced pore pressure prediction

Abstract

Wave cyclic loading in submarine sediments can lead to pore pressure accumulation, causing geohazards and compromising seabed stability. Accurate prediction of long-term wave-induced pore pressure is essential for disaster prevention. Although numerical simulations have contributed to understanding wave-induced pore pressure response, traditional methods lack the ability to simulate long-term and real oceanic conditions. This study proposes the use of recurrent neural network (RNN) models to predict wave-induced pore pressure based on in-situ monitoring data. Three RNN models (RNN, LSTM, and GRU) are compared, considering different seabed depths, and input parameters. The results demonstrate that all three RNN models can accurately predict wave-induced pore pressure data, with the GRU model exhibiting the highest accuracy (absolute error less than 2 kPa). Pore pressure at the previous time step and water depth are highly correlated with prediction, while wave height, wind speed, and wind direction show a secondary correlation. This study contributes to the development of wave-induced liquefaction early warning systems and offers insights for utilizing RNNs in geological time series analysis.