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Existing predictive models focus on predicting the displacement of a single monitoring point based on time series data, without considering spatial correlations among monitoring points, which makes it difficult to reveal the displacement changes in the entire monitoring system and ignores the potential threats from nonselected points. To address the above problem, this paper presents a novel deep learning method for predicting the slope deformation, by considering the spatial correlations between all points in the entire displacement monitoring system. The essential idea behind the proposed method is to predict the slope deformation based on the global information (i.e., the correlated displacements of all points in the entire monitoring system), rather than based on the local information (i.e., the displacements of a specified single point in the monitoring system). In the proposed method, (1) a weighted adjacency matrix is built to interpret the spatial correlations between all points, (2) a feature matrix is assembled to store the time-series displacements of all points, and (3) one of the state-of-the-art deep learning models, i.e., T-GCN, is developed to process the above graph-structured data consisting of two matrices. The effectiveness of the proposed method is verified by performing predictions based on a real dataset. The proposed method can be applied to predict time-dependency information in other similar geohazard scenarios, based on time-series data collected from multiple monitoring points.<\/jats:p>","DOI":"10.1007\/s00521-021-06084-6","type":"journal-article","created":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T09:02:51Z","timestamp":1620896571000},"page":"14441-14457","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["A deep learning approach using graph convolutional networks for slope deformation prediction based on time-series displacement data"],"prefix":"10.1007","volume":"33","author":[{"given":"Zhengjing","family":"Ma","sequence":"first","affiliation":[]},{"given":"Gang","family":"Mei","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0401-8422","authenticated-orcid":false,"given":"Edoardo","family":"Prezioso","sequence":"additional","affiliation":[]},{"given":"Zhongjian","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Nengxiong","family":"Xu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,13]]},"reference":[{"key":"6084_CR1","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Zhifeng\u00a0aro C, Corrado G, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia Y, Kaiser L, Kudlur M, Levenberg J, Zheng X (2016) Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467"},{"key":"6084_CR2","doi-asserted-by":"publisher","unstructured":"Abedjan Z, Golab L, Naumann F (2017) Data profiling\u2014a tutorial. 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