Computer Science ›› 2022, Vol. 49 ›› Issue (8): 33-39.doi: 10.11896/jsjkx.210600161

• Database & Big Data & Data Science • Previous Articles     Next Articles

Spatio-Temporal Attention-based Kriging for Land Deformation Data Interpolation

LI Rong-fan, ZHONG Ting, WU Jin, ZHOU Fan, KUANG Ping   

  1. School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China
  • Received:2021-06-21 Revised:2021-10-15 Published:2022-08-02
  • About author:LI Rong-fan,born in 1998,postgra-duate.His main research interests include graph neural network and data mining.
    ZHOU Fan,born in 1981,Ph.D,asso-ciate professor,is a member of China Computer Federation.His main research interests include machine lear-ning,spatio-temporal data mining,data mining and knowledge discovery.
  • Supported by:
    National Natural Science Foundation of China(62072077),National Key R & D Program of China(2019YFB1406202) and Sichuan Science and Technology Program(2020YFG0234).

Abstract: Landslide is one of the most common geological hazards,it causes significant damage to people’s life and property everyyear.In order to prevent and control landslides,it is necessary to monitor the land surface extensively.However,insurmountable difficulties such as severe climate and high monitoring cost impede the collection of land surface data,resulting in incomplete local data,unbalanced data sampling and dynamic changes of monitoring points,which hinder the prevention and control research of landslide and put forward new demand to the data collection and analysis.Existing methods try to handle incomplete data from spatial perspective,which,however,ignore temporal dependencies that are important for data interpolation.To solve the above problems,the incomplete INSAR data filling is studied,the spatio-temporal dependence is modeled by using the spatio-temporal mask matrix,the multi-level spatial relationship is comprehensively studied by using multi-head attention,and a novel Kriging interpolation method using spatio-temporal attention is proposed on the basis of Kriging.It realizes the deep understanding of complex temporal and spatial features.Interpolation experiments on real-world INSAR datasets show that the proposed model is capable to learn sophisticated spatial and temporal features effectively,and achieves better performance than the state-of-the-art methods in three different data interpolation scenarios.

Key words: Interpolation, Kriging, Landslide, Spatio-Temporal attention, Spatio-Temporal data mining

CLC Number: 

  • TP183
[1]BOZZANO F,CIPRIANI I,MAZZANTI P,et al.Displacement patterns of a landslide affected by human activities:insights from ground-based InSAR monitoring[J].Natural Hazards,2011,59(3):1377-1396.
[2]GAO W,DAI S,CHEN X.Landslide prediction based on a combination intelligent method using the GM and ENN:two cases of landslides in the Three Gorges Reservoir,China[J].Landslides,2020,17(1):111-126.
[3]HAJIMORADLOU A,ROBERTI G,POOLE D.Predicting Land-slides Using Locally Aligned Convolutional Neural Networks[J].arXiv:1911.04651,2019.
[4]HUANG R Q.Large-scale landslides and their sliding mechanisms in China since the 20th century [J].Chinese Journal of Rock Mechanics and Engineering,2007(3):433-454.
[5]ZHU A X,WANG R,QIAO J,et al.An expert knowledge-based approach to landslide susceptibility mapping using GIS and fuzzy logic[J].Geomorphology,2014,214:128-138.
[6]VAKHSHOORI V,ZARE M.Landslide susceptibility mapping by comparing weight of evidence,fuzzy logic,and frequency ratio methods[J].Geomatics,Natural Hazards and Risk,2016,7(5):1731-1752.
[7]ZHOU J,LU P,YANG Y.Reservoir landslides and its hazard effects for the hydropower station:a case study[C]//Workshop on World Landslide Forum.Cham:Springer,2017:699-706.
[8]GAN B R,YANG X G,ZHOU J W.GIS-based remote sensing analysis of the spatial-temporalevolution of landslides in a hydropower reservoir in southwest China[J].Geomatics,Natural Hazards and Risk,2019,10(1):2291-2312.
[9]CHEN W,XIE X,PENG J,et al.GIS-based landslide susceptibility modelling:a comparative assessment of kernel logistic regression,NaÏve-Bayes tree,and alternating decision tree models[J].Geomatics,Natural Hazards and Risk,2017,8(2):950-973.
[10]KALANTAR B,PRADHAN B,NAGHIBI S A,et al.Assess-ment of the effects of training data selection on the landslide susceptibility mapping:a comparison between support vector machine(SVM),logistic regression(LR) and artificial neural networks(ANN)[J].Geomatics,Natural Hazards and Risk,2018,9(1):49-69.
[11]HONG H,POURGHASEMI H R,POURTAGHI Z S.Land-slide susceptibility assessment in Lianhua County(China):a comparison between a random forest data mining technique and bivariate and multivariate statistical models[J].Geomorphology,2016,259:105-118.
[12]HONG H,PRADHAN B,JEBUR M N,et al.Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines[J].Environmental Earth Sciences,2016,75(1):1-14.
[13]LEI T,ZHANG Y,LV Z,et al.Landslide inventory mappingfrom bitemporal images using deep convolutional neural networks[J].IEEE Geoscience and Remote Sensing Letters,2019,16(6):982-986.
[14]YU B,YIN H,ZHU Z.Spatio-temporal graph convolutional networks:a deep learning framework for traffic forecasting[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence.2018:3634-3640.
[15]LI Y,YU R,SHAHABI C,et al.Diffusion Convolutional Recurrent Neural Network:Data-Driven Traffic Forecasting[C]//International Conference on Learning Representations.2018:1-16.
[16]WANG X,MA Y,WANG Y,et al.Traffic flow prediction via spatial temporal graph neural network[C]//Proceedings of the Web Conference.2020:1082-1092.
[17]HAMILTON W L,YING R,LESKOVEC J.Inductive representation learning on large graphs[J].arXiv:1706.02216,2017.
[18]CHIANG W L,LIU X,SI S,et al.Cluster-gcn:An efficient algorithm for training deep and large graph convolutional networks[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2019:257-266.
[19]ZENG H,ZHOU H,SRIVASTAVA A,et al.GraphSAINT:Graph Sampling Based Inductive Learning Method[C]//International Conference on Learning Representations.2019:1-19.
[20]VASWANI A,SHAZEER N,PARMAR N,et al.Attention is All you Need[C]//Conference on Neural Information Proces-sing Systems.2017:6000-6010.
[21]VELIČKOVIĆ P,CUCURULL G,CASANOVA A,et al.Graph attention networks[J].arXiv:1710.10903,2017.
[22]DONG J,ZHANG L,LIAO M,et al.Improved correction of seasonal tropospheric delay in InSAR observations for landslide deformation monitoring[J].Remote Sensing of Environment,2019,233:111370.
[23]CARLÀ T,INTRIERI E,RASPINI F,et al.Perspectives on the prediction of catastrophic slope failures from satellite InSAR[J].Scientific Reports,2019,9(1):1-9.
[24]LE N D,ZIDEK J V.Statistical Analysis of EnvironmentalSpace-Time Processes[J].Journal of the American Statistical Association,2007,102:1477-1477.
[25]KIPF T N,WELLING M.Semi-supervised classification withgraph convolutional networks[J].arXiv:1609.02907,2016.
[26]WU Z,PAN S,CHEN F,et al.A comprehensive survey ongraph neural networks[J].IEEE Transactions on Neural Networks and Learning Systems,2021,32(1):4-24.
[27]SONG S J,LAN C L,XING J L,et al.An end-to-end spatio-temporal attention model for human action recognition from skeleton data[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2017:4263-4270.
[28]ZHAO Z,YANG Q,CAI D,et al.Video Question Answering via Hierarchical Spatio-Temporal Attention Networks[C]//International Joint Conference on Artificial Intelligence.2017:3518-3524.
[29]CRESSIE N.The origins of kriging[J].Mathematical Geology,1990,22(3):239-252.
[30]TAPOGLOU E,KARATZAS G P,TRICHAKIS I C,et al.Aspatio-temporal hybrid neural network-Kriging model for groundwater level simulation[J].Journal of Hydrology,2014,519:3193-3203.
[31]FRANCHI G,YAO A,KOLB A.Supervised deep Kriging for single-image super-resolution[C]//German Conference on Pattern Recognition.Cham:Springer,2018:638-649.
[32]WU Y,ZHUANG D,LABBE A,et al.Inductive graph neural networks for spatiotemporal kriging[J].arXiv:2006.07527,2020.
[33]ZHOU T,SHAN H,BANERJEE A,et al.Kernelized probabilistic matrix factorization:Exploiting graphs and side information[C]//Proceedings of the 2012 SIAM International Confe-rence on Data mining.Society for Industrial and Applied Mathematics,2012:403-414.
[1] GAO Yue, FU Xiang-ling, OUYANG Tian-xiong, CHEN Song-ling, YAN Chen-wei. EEG Emotion Recognition Based on Spatiotemporal Self-Adaptive Graph ConvolutionalNeural Network [J]. Computer Science, 2022, 49(4): 30-36.
[2] MA Jun-cheng, JIANG Mu-rong, FANG Su-qin. Three-dimensional Reconstruction of Cone Meteorological Data Based on Improved MarchingTetrahedra Algorithm [J]. Computer Science, 2021, 48(11A): 644-647.
[3] LIU Ya-chen, HUANG Xue-ying. Research on Creep Feature Extraction and Early Warning Algorithm Based on Satellite MonitoringSpatial-Temporal Big Data [J]. Computer Science, 2021, 48(11A): 258-264.
[4] YOU Lan, HAN Xue-wei, HE Zheng-wei, XIAO Si-yu, HE Du, PAN Xiao-meng. Improved Sequence-to-Sequence Model for Short-term Vessel Trajectory Prediction Using AIS Data Streams [J]. Computer Science, 2020, 47(9): 169-174.
[5] GAO Qiang, GAO Jing-yang, ZHAO Di. GNNI U-net:Precise Segmentation Neural Network of Left Ventricular Contours for MRI Images Based on Group Normalization and Nearest Interpolation [J]. Computer Science, 2020, 47(8): 213-220.
[6] TIAN Wei-wei, ZHOU Yue, YIN Wang, HE Ling, DENG Li-hua and LI Yuan-yuan. Automatic Voice Detection Algorithm for Schizophrenic Combining EHHT and CI [J]. Computer Science, 2020, 47(6A): 187-195.
[7] LIU Chang-yun,YANG Yu-di,ZHOU Li-hua,ZHAO Li-hong. Discovering Popular Social Location with Time Label [J]. Computer Science, 2019, 46(7): 186-194.
[8] SONG Gang, DU Hong-wei, WANG Ping, LIU Xin-xin, HAN Hui-jian. Texture Detail Preserving Image Interpolation Algorithm [J]. Computer Science, 2019, 46(6A): 169-176.
[9] DENG Guo-qiang, TANG Min, LIANG Zhuang-chang. Divide-and-Conquer Algorithm for Sparse Polynomial Interpolation [J]. Computer Science, 2019, 46(5): 298-303.
[10] MAO Ying-chi, CAO Hai, HE Jin-feng. Spatio-Temporal Integrated Forecasting Algorithm for Dam Deformation [J]. Computer Science, 2019, 46(2): 223-229.
[11] ZHANG Jie, WANG Gang, YAO Xiao-qiang, SONG Ya-fei, ZHENG Kang-bo. Research on Track Fitting Model Under Two-way RNN [J]. Computer Science, 2019, 46(11A): 58-61.
[12] QIAN Jiang, WANG Fan and GUO Qing-jie. Bivariate Non-tensor-product-typed Continued Fraction Interpolation [J]. Computer Science, 2018, 45(3): 83-91.
[13] LIU Cheng-zhi, HAN Xu-li and LI Jun-cheng. Selection of Control Points of Quadratic-trigonometric Hermite Interpolation Splines [J]. Computer Science, 2018, 45(3): 76-82.
[14] LIU Tian-tian, BAO Fang-xun, ZHANG Yun-feng, FAN Qing-lan and YANG Xiao-mei. Rational Fractal Surface Modeling and Its Application in Image Super-resolution [J]. Computer Science, 2018, 45(3): 35-45.
[15] ZHANG Zhi-guo, ZHENG Xi and LAN Jing-chuan. Image Edge Detection Based on Pyramidal Algorithm of Interpolation Wavelet [J]. Computer Science, 2017, 44(Z6): 164-168.
Full text



No Suggested Reading articles found!