首页|A graph deep learning method for landslide displacement prediction based on global navigation satellite system positioning
A graph deep learning method for landslide displacement prediction based on global navigation satellite system positioning
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NETL
NSTL
万方数据
维普
The accurate prediction of displacement is crucial for landslide deformation monitoring and early warn-ing.This study focuses on a landslide in Wenzhou Belt Highway and proposes a novel multivariate land-slide displacement prediction method that relies on graph deep learning and Global Navigation Satellite System(GNSS)positioning.First model the graph structure of the monitoring system based on the engi-neering positions of the GNSS monitoring points and build the adjacent matrix of graph nodes.Then con-struct the historical and predicted time series feature matrixes using the processed temporal data including GNSS displacement,rainfall,groundwater table and soil moisture content and the graph struc-ture.Last introduce the state-of-the-art graph deep learning GTS(Graph for Time Series)model to improve the accuracy and reliability of landslide displacement prediction which utilizes the temporal-spatial dependency of the monitoring system.This approach outperforms previous studies that only learned temporal features from a single monitoring point and maximally weighs the prediction perfor-mance and the priori graph of the monitoring system.The proposed method performs better than SVM,XGBoost,LSTM and DCRNN models in terms of RMSE(1.35 mm),MAE(1.14 mm)and MAPE(0.25)evaluation metrics,which is provided to be effective in future landslide failure early warning.
Landslide displacement predictionGNSS positioningGraph deep learning
Chuan Yang、Yue Yin、Jiantong Zhang、Penghui Ding、Jian Liu
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China Transport Telecommunications and Information Center,Beijing 100011,PR China
China Nuclear Power Engineering Co.,Ltd,Beijing 100011,PR China
China Transport TeleCommunications and Information CenterWenzhou Transportation Development Group Co.LTD国家自然科学基金
NETL
NSTL
万方数据
维普
