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.

Improved Hierarchical Temporal Memory for Online Prediction of Ocean Time Series Data

“…This enables a large volume of continuous and high-temporal-resolution observation data acquisition, allowing for a better understanding of the evolution of marine spatial-temporal processes and significant improvements in the capability, accuracy, and efficiency of prediction of ocean phenomena. These observation data give researchers an opportunity to utilize various time series forecasting methods to predict observational parameters for both the ocean surface and the deep sea [5][6][7][8][9], for the purpose of enhancing insights into oceanic environmental conditions, marine spatial-temporal process evolution, and marine phenomena and disasters.…”

“…Regarding the existing deep learning models, RNN and its variants such as LSTM and gate recurrent units (GRUs) [8] have demonstrated high efficiency and superb prediction accuracy in long-term complex time series forecasting. They have been successfully adopted to predict sea surface temperature, sea-level height, sea ice, dissolved oxygen, chlorophyll, and pore water characteristics [5][6][7][8][9]18,19].…”

“…Regarding the existing deep learning models, RNN and its variants such as LSTM and gate recurrent units (GRUs) [8] have demonstrated high efficiency and superb prediction accuracy in long-term complex time series forecasting. They have been successfully adopted to predict sea surface temperature, sea-level height, sea ice, dissolved oxygen, chlorophyll, and pore water characteristics [5][6][7][8][9]18,19]. Moreover, the high scalability advantage enables these algorithms to effectively handle diverse types of complex and long-term time series data by either adjusting their scales and internal structures or integrating them with other methods [1,[3][4][5]19].…”