A Landslide Displacement Prediction Model Based on the ICEEMDAN Method and the TCN–BiLSTM Combined Neural Network

Abstract

Influenced by autochthonous geological conditions and external environmental changes, the evolution of landslides is mostly nonlinear. This article proposes a combined neural network prediction model that combines a temporal convolutional neural network (TCN) and a bidirectional long short-term memory neural network (BiLSTM) to address the shortcomings of traditional recurrent neural networks in predicting displacement-fluctuation-type landslides. Based on the idea of time series decomposition, the improved complete ensemble empirical mode decomposition with an adaptive noise method (ICEEMDAN) was used to decompose displacement time series data into trend and fluctuation terms. Trend displacement is mainly influenced by the internal geological conditions of a landslide, and polynomial fitting is used to determine the future trend displacement; The displacement of the fluctuation term is mainly influenced by the external environment of landslides. This article selects three types of landslide-influencing factors: rainfall, groundwater level elevation, and the historical displacement of landslides. It uses a combination of gray correlation (GRG) and mutual information (MIC) correlation modules for feature screening. Then, TCN is used to extract landslide characteristic factors, and BiLSTM captures the relationship between features and displacement to achieve the prediction of wave term displacement. Finally, the trend term and fluctuation term displacement prediction values are reconstructed to obtain the total displacement prediction value. The results indicate that the ICEEMDAN–TCN–BiLSTM model proposed in this article can accurately predict landslide displacement and has high engineering application value, which is helpful for planning and constructing landslide disaster prevention projects.

Series Arc Fault Detection under Vibration Condition Based on N-M-M-B

“…The BiLSTM model is a time-recurrent neural network, which is composed of forward LSTM and reverse LSTM [23]. The signal is, respectively, input into two LSTM neural networks in positive and reverse order for feature extraction, and the two output vectors (that is, the extracted feature vectors) are spliced to form a vector as the final feature output [24,25]. The feature data obtained by the BiLSTM model at the t moment simultaneously have past and future information.…”

Data-Driven Deformation Prediction of Accumulation Landslides in the Middle Qinling-Bashan Mountains Area

“…Due to the abnormal power supply of equipment in the field, the failure of the equipment itself, or the weak communication signal causing the monitoring data to jump or be missing, the first step is to carry out the cleaning of landslide deformation monitoring data, including the filling of missing data and correction of abnormal data [38][39][40][41][42]. Due to the diversity of geohazard monitoring data, it is difficult to completely identify abnormal data with the same algorithm [43,44]. In order to identify the anomalous data, the long time series of monitoring data is processed using the method of region segmentation and the specific algorithm for different morphology data is used to identify the anomalies.…”