A deep learning approach using graph convolutional networks for slope deformation prediction based on time-series displacement data

Ma, Zhengjing; Mei, Gang; Prezioso, Edoardo; Zhang, Zhongjian; Xu, Nengxiong

doi:10.1007/s00521-021-06084-6

Cited by 48 publications

(23 citation statements)

References 60 publications

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“…Compared with LTSM, GRU has the advantages of fewer hyperparameters and faster training by using two new gates (update gate and reset gate) (Figure 3). These two gates are utilized to store as much information as possible for a long time series [49,50]. The reset gate is responsible for determining how much information at the previous moment is passed along, and resets the information at the current moment.…”

Section: Gated Recurrent Unitmentioning

confidence: 99%

Using Complementary Ensemble Empirical Mode Decomposition and Gated Recurrent Unit to Predict Landslide Displacements in Dam Reservoir

Yang

Xiao

Huang³

2022

Sensors

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It is crucial to predict landslide displacement accurately for establishing a reliable early warning system. Such a requirement is more urgent for landslides in the reservoir area. The main reason is that an inaccurate prediction can lead to riverine disasters and secondary surge disasters. Machine learning (ML) methods have been developed and commonly applied in landslide displacement prediction because of their powerful nonlinear processing ability. Recently, deep ML methods have become popular, as they can deal with more complicated problems than conventional ML methods. However, it is usually not easy to obtain a well-trained deep ML model, as many hyperparameters need to be trained. In this paper, a deep ML method—the gated recurrent unit (GRU)—with the advantages of a powerful prediction ability and fewer hyperparameters, was applied to forecast landslide displacement in the dam reservoir. The accumulated displacement was firstly decomposed into a trend term, a periodic term, and a stochastic term by complementary ensemble empirical mode decomposition (CEEMD). A univariate GRU model and a multivariable GRU model were employed to forecast trend and stochastic displacements, respectively. A multivariable GRU model was applied to predict periodic displacement, and another two popular ML methods—long short-term memory neural networks (LSTM) and random forest (RF)—were used for comparison. Precipitation, reservoir level, and previous displacement were considered to be candidate-triggering factors for inputs of the models. The Baijiabao landslide, located in the Three Gorges Reservoir Area (TGRA), was taken as a case study to test the prediction ability of the model. The results demonstrated that the GRU algorithm provided the most encouraging results. Such a satisfactory prediction accuracy of the GRU algorithm depends on its ability to fully use the historical information while having fewer hyperparameters to train. It is concluded that the proposed model can be a valuable tool for predicting the displacements of landslides in the TGRA and other dam reservoirs.

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Section: Gated Recurrent Unitmentioning

confidence: 99%

Using Complementary Ensemble Empirical Mode Decomposition and Gated Recurrent Unit to Predict Landslide Displacements in Dam Reservoir

Yang

Xiao

Huang³

2022

Sensors

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“…This study implements a similar architecture of T-GCN as the one proposed by [39], where further details about the T-GCN model can be found. Furthermore, similar T-GCN models have also been successfully designed [47]. The Keras version 2.6.0, TensorFlow version 2.6.0, and Spektral version 1.0.8 [48,49] libraries of Python have been used to build the model practically.…”

Section: Temporal-graph Convolutional Neural Networkmentioning

confidence: 99%

“…WNTR is an open-source Python library based on EPANET that integrates hydraulic simulation, water quality, and several metric options for the comprehensive resilience assessment of a water network. It allows us to generate and modify the structure of water networks, simulates different analysis scenarios and response strategies, simulates and analyzes network pressure-dependent demands, analyzes water quality, calculates resilience metrics, and visualizes the results [47].…”

Section: Data Set Generation For T-gcn Applicationmentioning

confidence: 99%

A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation

Granados

Zanfei

Brentan

et al. 2022

Water

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Water distribution system monitoring is currently carried out using advanced real-time control technologies to achieve a higher operational efficiency. Data analysis techniques can be implemented for condition estimation, which are crucial tools for managing, developing, and operating water networks using the monitored flow rate and pressure data at some network pipes and nodes. This work proposes a state estimation methodology that enables one to infer the hydraulic state of the operating speed of pumping systems from these pressure and flow measurements. The presented approach suggests using graph convolutional neural network theory linked to hydraulic models for generating a digital twin of the water system. It is validated on two benchmark hydraulic networks: the Patios-Villa del Rosario, Colombia, and the C-Town networks. The results show that the proposed model effectively predicts the state estimation in the two hydraulic networks used. The results of the evaluation metrics indicate low values of mean squared error and mean absolute error and high values of the coefficient of determination, reflecting high predictive ability and that the prediction results adequately represent the real data.

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“…More and more process data is generated in non-Euclidean domains and expressed as graphs with complex associations and interdependencies between nodes. The main characteristics of graph data are regarded as that there is a connection between different pair of nodes, which shows the nodes are not completely independent [22]. Dynamic graphs emphasize the sequences of appearance of nodes and edges are strongly coupled to time.…”

Section: Spatia-temporal Graphsmentioning

confidence: 99%

Spatial-temporal associations representation and application for process monitoring using graph convolution neural network

Ren¹,

Yang²,

Liang³

et al. 2022

Preprint

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Industrial process data reflects the dynamic changes of operation conditions, which mainly refer to the irregular changes in the dynamic associations between different variables in different time. And this related associations knowledge for process monitoring is often implicit in these dynamic monitoring data which always have richer operation condition information and have not been paid enough attention in current research. To this end, a new process monitoring method based on spatial-based graph convolution neural network (SGCN) is proposed to describe the characteristics of the dynamic associations which can be used to represent the operation status over time. Spatia-temporal graphs are firstly defined, which can be used to represent the characteristics of node attributes (dynamic edge features) dynamically changing with time. Then, the associations between monitoring variables at a certain time can be considered as the node attributes to define a snapshot of the static graph network at the certain time. Finally, the snapshot containing graph structure and node attributes is used as model inputs which are processed to implement graph classification by spatial-based convolution graph neural network with aggregate and readout steps. The feasibility and applicability of this proposed method are demonstrated by our experimental results of benchmark and practical case application.

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A deep learning approach using graph convolutional networks for slope deformation prediction based on time-series displacement data

Cited by 48 publications

References 60 publications

Using Complementary Ensemble Empirical Mode Decomposition and Gated Recurrent Unit to Predict Landslide Displacements in Dam Reservoir

Using Complementary Ensemble Empirical Mode Decomposition and Gated Recurrent Unit to Predict Landslide Displacements in Dam Reservoir

A Digital Twin of a Water Distribution System by Using Graph Convolutional Networks for Pump Speed-Based State Estimation

Spatial-temporal associations representation and application for process monitoring using graph convolution neural network

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