447
Views
2
CrossRef citations to date
0
Altmetric
Articles

Spatiotemporal prediction of landslide displacement using deep learning approaches based on monitored time-series displacement data: a case in the Huanglianshu landslide

, ORCID Icon, , & ORCID Icon
Pages 98-113 | Received 31 May 2022, Accepted 16 Jan 2023, Published online: 09 Feb 2023

References

  • Abadi, M. 2016. “TensorFlow: Learning Functions at Scale.” Acm Sigplan Notices 51 (9): 1–1. doi:10.1145/3022670.2976746
  • Baum, R., and J. Godt. 2010. “Early Warning of Rainfall-Induced Shallow Landslides and Debris Flows in the USA.” Landslides 7 (3): 259–272. doi:10.1007/s10346-009-0177-0
  • Benoit, L., P. Briole, O. Martin, C. Thom, J. P. Malet, and P. Ulrich. 2015. “Monitoring Landslide Displacements with the Geocube Wireless Network of Low-Cost GPS.” Engineering Geology 195: 111–121. doi:10.1016/j.enggeo.2015.05.020
  • Cai, Z. L., W. Y. Xu, Y. D. Meng, C. Shi, and R. B. Wang. 2016. “Prediction of Landslide Displacement Based on GA-LSSVM with Multiple Factors.” Bulletin of Engineering Geology and the Environment 75 (2): 637–646. doi:10.1007/s10064-015-0804-z
  • Caspi, I. 2017. “Rtadf: Testing for Bubbles with EViews.” Journal of Statistical Software 81 (CN1): 1–16. doi:10.18637/jss.v081.c01.
  • Chai, T., and R. R. Draxler. 2014. “Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)? – Arguments Against Avoiding RMSE in the Literature.” Geoscientific Model Development 7 (3): 1247–1250. doi:10.5194/gmd-7-1247-2014
  • Chan, W., Y. F. Yung, and P. M. Bentler. 1995. “A Note on Using and Unbiased Weight Matrix in the ADF Test Statistic.” Multivariate Behavioral Research 30 (4): 453–459. doi:10.1207/s15327906mbr3004_1
  • Dai, F. C., and C. F. Lee. 2002. “Landslide Characteristics and Slope Instability Modeling Using GIS, Lantau Island, Hong Kong.” Geomorphology 42 (3-4): 213–228. doi:10.1016/S0169-555X(01)00087-3
  • Das, S., and D. N. Politis. 2021. “Predictive Inference for Locally Stationary Time Series with an Application to Climate Data.” Journal of the American Statistical Association 116 (534): 919–934. doi:10.1080/01621459.2019.1708368
  • Du, J., K. L. Yin, and S. Lacasse. 2013. “Displacement Prediction in Colluvial Landslides, Three Gorges Reservoir, China.” Landslides 10 (2): 203–218. doi:10.1007/s10346-012-0326-8
  • Eidsvig, U. M. K., M. Papathoma-Kohle, J. Du, T. Glade, and B. V. Vangelsten. 2014. “Quantification of Model Uncertainty in Debris Flow Vulnerability Assessment.” Engineering Geology 181: 15–26. doi:10.1016/j.enggeo.2014.08.006
  • Froude, M. J., and D. N. Petley. 2018. “Global Fatal Landslide Occurrence from 2004 to 2016.” Natural Hazards and Earth System Sciences 18 (8): 2161–2181. doi:10.5194/nhess-18-2161-2018
  • Gariano, S. L., and F. Guzzetti. 2016. “Landslides in a Changing Climate.” Earth-Science Reviews 162: 227–252. doi:10.1016/j.earscirev.2016.08.011
  • Guzzetti, F., S. L. Gariano, S. Peruccacci, M. T. Brunetti, I. Marchesini, M. Rossi, and M. Melillo. 2020. “Geographical Landslide Early Warning Systems.” Earth-Science Reviews 200, 102973. doi:10.1016/j.earscirev.2019.102973
  • Hammond, D. K., P. Vandergheynst, and R. Gribonval. 2011. “Wavelets on Graphs via Spectral Graph Theory.” Applied and Computational Harmonic Analysis 30 (2): 129–150. doi:10.1016/j.acha.2010.04.005
  • Hindriks, R., M. H. Adhikari, Y. Murayama, M. Ganzetti, D. Mantini, N. K. Logothetis, and G. Deco. 2016. “Can Sliding-Window Correlations Reveal Dynamic Functional Connectivity in Resting-State fMRI?” Neuroimage 127: 242–256. doi:10.1016/j.neuroimage.2015.11.055
  • Hochreiter, S., and J. Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735
  • Huang, F. M., J. S. Huang, S. H. Jiang, and C. B. Zhou. 2017. “Landslide Displacement Prediction Based on Multivariate Chaotic Model and Extreme Learning Machine.” Engineering Geology 218: 173–186. doi:10.1016/j.enggeo.2017.01.016
  • Huang, F. M., J. Zhang, C. B. Zhou, Y. H. Wang, J. S. Huang, and L. Zhu. 2020. “A Deep Learning Algorithm Using a Fully Connected Sparse Autoencoder Neural Network for Landslide Susceptibility Prediction.” Landslides 17 (1): 217–229. doi:10.1007/s10346-019-01274-9
  • Intrieri, E., G. Gigli, F. Mugnai, R. Fanti, and N. Casagli. 2012. “Design and Implementation of a Landslide Early Warning System.” Engineering Geology 147-148: 124–136. doi:10.1016/j.enggeo.2012.07.017
  • Jaboyedoff, M., T. Oppikofer, A. Abellan, M. H. Derron, A. Loye, R. Metzger, and A. Pedrazzini. 2012. “Use of LIDAR in Landslide Investigations: A Review.” Natural Hazards 61 (1): 5–28. doi:10.1007/s11069-010-9634-2
  • LeCun, Y., Y. Bengio, and G. Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436–444. doi:10.1038/nature14539
  • Li, S. H., L. Z. Wu, J. J. Chen, and R. Q. Huang. 2020. “Multiple Data-Driven Approach for Predicting Landslide Deformation.” Landslides 17 (3): 709–718. doi:10.1007/s10346-019-01320-6
  • Li, H. J., L. Zhu, Z. X. Dai, H. L. Gong, T. Guo, G. X. Guo, J. B. Wang, and P. Teatini. 2021. “Spatiotemporal Modeling of Land Subsidence Using a Geographically Weighted Deep Learning Method Based on PS-InSAR.” Science of the Total Environment 799: 1–13. doi:10.1016/j.scitotenv.2021.149244.
  • Lin, L., Z. B. He, and S. Peeta. 2018. “Predicting Station-Level Hourly Demand in a Large-Scale Bike Sharing Network: A Graph Convolutional Neural Network Approach.” Transportation Research Part C: Emerging Technologies 97: 258–276. doi:10.1016/j.trc.2018.10.011
  • Liu, Y., Z. Chen, B. D. Hu, J. K. Jin, and Z. Wu. 2019. “A non-Uniform Spatiotemporal Kriging Interpolation Algorithm for Landslide Displacement Data.” Bulletin of Engineering Geology and the Environment 78 (6): 4153–4166. doi:10.1007/s10064-018-1388-1
  • Ma, Z. J., and G. Mei. 2021. “Deep Learning for Geological Hazards Analysis: Data, Models, Applications, and Opportunities.” Earth-Science Reviews 223: 1–33. doi:10.1016/j.earscirev.2021.103858.
  • Ma, Z. J., G. Mei, E. Prezioso, Z. J. Zhang, and N. X. Xu. 2021. “A Deep Learning Approach Using Graph Convolutional Networks for Slope Deformation Prediction Based on Time-Series Displacement Data.” Neural Computing and Applications 33 (21): 14441–14457. doi:10.1007/s00521-021-06084-6
  • Malet, J. P., O. Maquaire, and E. Calais. 2002. “The use of Global Positioning System Techniques for the Continuous Monitoring of Landslides: Application to the Super-Sauze Earthflow (Alpes-de-Haute-Provence, France).” Geomorphology 43 (1-2): 33–54. doi:10.1016/S0169-555X(01)00098-8
  • Niu, Z. Y., G. Q. Zhong, and H. Yu. 2021. “A Review on the Attention Mechanism of Deep Learning.” Neurocomputing 452: 48–62. doi:10.1016/j.neucom.2021.03.091
  • Pei, H. F., F. H. Meng, and H. H. Zhu. 2021. “Landslide Displacement Prediction Based on a Novel Hybrid Model and Convolutional Neural Network Considering Time-Varying Factors.” Bulletin of Engineering Geology and the Environment 80 (10): 7403–7422. doi:10.1007/s10064-021-02424-x
  • Phoon, K. K., and W. G. Zhang. 2022. “Future of Machine Learning in Geotechnics.” Georisk-Assessment and Management of Risk for Engineered Systems and Geohazards. doi:10.1080/17499518.2022.2087884.
  • Reichenbach, P., M. Rossi, B. D. Malamud, M. Mihir, and F. Guzzetti. 2018. “A Review of Statistically-Based Landslide Susceptibility Models.” Earth-Science Reviews 180: 60–91. doi:10.1016/j.earscirev.2018.03.001
  • Ren, Y. J., D. Zhao, D. Luo, H. D. Ma, and P. R. Duan. 2022. “Global-Local Temporal Convolutional Network for Traffic Flow Prediction.” IEEE Transactions on Intelligent Transportation Systems 23 (2): 1578–1584. doi:10.1109/TITS.2020.3025076
  • Santoso, A. M., K. K. Phoon, and S. T. Quek. 2011. Effects of Soil Spatial Variability on Rainfall-Induced Landslides. Computers and Structures 89 (11-12): 893–900. doi:10.1016/j.compstruc.2011.02.016.
  • Segalini, A., A. Valletta, and A. Carri. 2018. “Landslide Time-of-Failure Forecast and Alert Threshold Assessment: A Generalized Criterion.” Engineering Geology 245: 72–80. doi:10.1016/j.enggeo.2018.08.003
  • Sharifi, S., R. Macciotta, and M. T. Hendry. 2022. “Algorithms to Enhance Detection of Landslide Acceleration Moment and Time-to-Failure Forecast Using Time-Series Displacements.” Engineering Geology 106832: 1–21. doi:10.1016/j.enggeo.2022.106832.
  • Shorten, C., and T. M. Khoshgoftaar. 2019. “A Survey on Image Data Augmentation for Deep Learning.” Journal of Big Data 6: 1–48. doi:10.1186/s40537-019-0197-0.
  • Sorjamaa, A., J. Hao, N. Reyhani, Y. N. Ji, and A. Lendasse. 2007. “Methodology for Long-Term Prediction of Time Series.” Neurocomputing 70 (16-18): 2861–2869. doi:10.1016/j.neucom.2006.06.015
  • Wang, J., G. G. Nie, S. J. Gao, S. G. Wu, H. Y. Li, and X. B. Ren. 2021. “Landslide Deformation Prediction Based on a GNSS Time Series Analysis and Recurrent Neural Network Model.” Remote Sensing 13 (6): 1–16. doi:10.3390/rs13061055.
  • Wei, R. L., C. M. Ye, Y. G. Ge, and Y. Li. 2022. An Attention-Constrained Neural Network with Overall Cognition for Landslide Spatial Prediction.” Landslides 19 (5): 1087–1099. doi:10.1007/s10346-021-01841-z.
  • Wu, Z. H., S. R. Pan, F. W. Chen, G. D. Long, C. Q. Zhang, and P. S. Yu. 2021. “A Comprehensive Survey on Graph Neural Networks.” Ieee Transactions on Neural Networks and Learning Systems 32 (1): 4–24. doi:10.1109/TNNLS.2020.2978386
  • Xiao, T., L. M. Zhang, R. W. M. Cheung, and S. Lacasse. 2022. “Predicting Spatio-Temporal Man-Made Slope Failures Induced by Rainfall in Hong Kong Using Machine Learning Techniques.” Geotechnique 0: 1–17. doi:10.1680/jgeot.21.00160.
  • Xu, W. H., H. Xu, J. Chen, Y. F. Kang, Y. Y. Pu, Y. B. Ye, and J. Tong. 2022. “Combining Numerical Simulation and Deep Learning for Landslide Displacement Prediction: An Attempt to Expand the Deep Learning Dataset.” Sustainability 14 (11). doi:10.3390/su14116908.
  • Yang, B. B., K. L. Yin, S. Lacasse, and Z. Q. Liu. 2019. “Time Series Analysis and Long Short-Term Memory Neural Network to Predict Landslide Displacement.” Landslides 16 (4): 677–694. doi:10.1007/s10346-018-01127-x
  • Yang, H. Q., L. Zhang, L. Gao, K. K. Phoon, and X. Wei. 2022. “On the Importance of Landslide Management: Insights from a 32-Year Database of Landslide Consequences and Rainfall in Hong Kong.” Engineering Geology 299. doi:10.1016/j.enggeo.2022.106578.
  • Yusuf, M., and K. A. Sugeng. 2018. “The Relation between the Square of the Adjacency Matrix and Spectra of the Distance Matrix of a Graph with Diameter Two.” 8th annual Basic Science International Conference (BaSIC) - Coverage of Basic Sciences toward the World’s Sustainability Challanges, Malang, INDONESIA.
  • Zhang, W. A., X. Gu, L. B. Tang, Y. P. Yin, D. S. Liu, and Y. M. Zhang. 2022. “Application of Machine Learning, Deep Learning and Optimization Algorithms in Geoengineering and Geoscience: Comprehensive Review and Future Challenge.” Gondwana Research 109: 1–17. doi:10.1016/j.gr.2022.03.015
  • Zhang, Y. G., J. Tang, Z. Y. He, J. K. Tan, and C. Li. 2021. “A Novel Displacement Prediction Method Using Gated Recurrent Unit Model with Time Series Analysis in the Erdaohe Landslide.” Natural Hazards 105 (1): 783–813. doi:10.1007/s11069-020-04337-6
  • Zhang, K., K. Zhang, C. X. Cai, W. L. Liu, and J. B. Xie. 2021. “Displacement Prediction of Step-Like Landslides Based on Feature Optimization and VMD-Bi-LSTM: A Case Study of the Bazimen and Baishuihe Landslides in the Three Gorges, China.” Bulletin of Engineering Geology and the Environment 80 (11): 8481–8502. doi:10.1007/s10064-021-02454-5
  • Zhao, L., Y. J. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, M. Deng, and H. F. Li. 2020. “T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction.” Ieee Transactions on Intelligent Transportation Systems 21 (9): 3848–3858. doi:10.1109/TITS.2019.2935152
  • Zou, Z. X., Y. M. Yang, Z. Q. Fan, H. M. Tang, M. Zou, X. L. Hu, C. R. Xiong, and J. W. Ma. 2020. “Suitability of Data Preprocessing Methods for Landslide Displacement Forecasting.” Stochastic Environmental Research and Risk Assessment 34 (8): 1105–1119. doi:10.1007/s00477-020-01824-x

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.