Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model

Jing Wang; Guigen Nie; Shengjun Gao; Shuguang Wu; Haiyang Li; Xiaobing Ren

Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model

2021

The prediction of landslide displacement is a challenging and essential task. It is thus very important to choose a suitable displacement prediction model. This paper develops a novel Attention Mechanism with Long Short Time Memory Neural Network (AMLSTM NN) model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) landslide displacement prediction. The CEEMDAN method is implemented to ingest landslide Global Navigation Satellite System (GNSS) time series. The AMLSTM algorithm is then used to realize prediction work, jointly with multiple impact factors. The Baishuihe landslide is adopted to illustrate the capabilities of the model. The results show that the CEEMDAN-AMLSTM model achieves competitive accuracy and has significant potential for landslide displacement prediction.

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