Landslide Displacement Prediction Based on Transfer Learning and Bi-GRU

Abstract

Predicting slope deformation prediction is crucial for early warning of slope failure, preventing damage to properties, and saving human lives. However, in practice, equipment maintenance causes discontinuity in the displacement data, and the traditional prediction models based on deep networks do not perform well in this case. To solve the problem of prediction accuracy in case of discontinuous and inadequate data, we propose a combined displacement prediction model that integrates the bidirectional gated recurrent unit (Bi-GRU), attention mechanism, and transfer learning. The Bi-GRU is employed to extract the forward and backward characteristics of displacement series, and the attention mechanism is utilized to give different weights to the extracted information so as to highlight the critical information. Transfer learning is used to guarantee prediction accuracy in case of discontinuous and limited data. The model is then employed to predict the slope displacement of the JinYu Cement Plant in China. Finally, the modeling results excellently agree with measured displacement, especially in case of insufficient sample data.

Towards next-generation NDE: predictive analysis of pipeline integrity using machine learning

“…25 The enhancement is achieved by adding a second layer in the traditional GRU architecture, which helps obtain the past and future states for enhanced prediction outcomes. 26 The LSTM neural network has the unique capabilities to model dependencies effectively over extended periods. Rather than using a recurrent neuron, the structure of LSTM employs a recurrent module, which includes a memory cell to retain the information.…”