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
-
摘要: The accurate prediction of displacement is crucial for landslide deformation monitoring and early warning. This study focuses on a landslide in Wenzhou Belt Highway and proposes a novel multivariate landslide 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 engineering positions of the GNSS monitoring points and build the adjacent matrix of graph nodes. Then construct 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 structure. 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 performance 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.Abstract: The accurate prediction of displacement is crucial for landslide deformation monitoring and early warning. This study focuses on a landslide in Wenzhou Belt Highway and proposes a novel multivariate landslide 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 engineering positions of the GNSS monitoring points and build the adjacent matrix of graph nodes. Then construct 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 structure. 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 performance 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.
-
-
[1] Bai, Z., Zhang, Q., Huang, G., Jing, C., Wang, J., 2019. Real-time BeiDou landslide monitoring technology of “light terminal plus industry cloud”. Acta Geod. et Cartogr. Sin. 48 (11), 1424-1429.
[2] Barzaghi, R., Cazzaniga, N.E., De Gaetani, C.I., Pinto, L., Tornatore, V., 2018. Estimating and comparing dam deformation using classical and GNSS techniques. Sensors 18(3), 756.
[3] Carlà, T., Intrieri, E., Raspini, F., 2019. Perspectives on the prediction of catastrophic slope failures from satellite InSAR. Sci. Rep. 9(1), 14137.
[4] Cenni, N., Fiaschi, S., Fabris, M., 2021. Integrated use of archival aerial photogrammetry, GNSS, and InSAR data for the monitoring of the Patigno landslide (Northern Apennines, Italy). Landslides 18, 2247-2263.
[5] Chang, Z., Du, Z., Zhang, F., Huang, F., Chen, J., Li, W., Guo, Z., 2020. Landslide susceptibility prediction based on remote sensing images and GIS: Comparisons of supervised and unsupervised machine learning models. Remote Sens. 12(3), 502.
[6] Chen, T., Guestrin, C., 2016. Xgboost: A scalable tree boosting system, in: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, August 13, 2016. San Francisco, California, USA, 785-794.
[7] Chen, Y., Wu, L., Zaki, M., 2020. Iterative deep graph learning for graph neural networks: Better and robust node embeddings. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (Eds.), Advances in Neural Information Processing Systems 33, 19314-19326.
[8] Du, J., Yin, K., Lacasse, S., 2013. Displacement prediction in colluvial landslides, Three Gorges Reservoir, China. Landslides 10(2), 203-218.
[9] Gao, Y., Chen, X., Tu, R., 2022. Application of dynamic optimization time-delay GM(1,2) model in landslide displacement prediction considering the influence of rainfall. Acta Geod. et Cartogr. Sin. 51(10), 2183-2195.
[10] He, Y., Miao, Y., Liu, H., 2016. Based on Beidou/GPS precise displacement monitoring technology in the application of the bridge monitoring. Journal of Yunnan University 38(S1), 35-39 (in Chinese with English abstract).
[11] He, C., Xu, Q., Ju, N., 2018. Real-time early warning technology of debris flow based on automatic identification of rainfall process. Journal of Engineering Geology 26(3), 703-710 (in Chinese with English abstract).
[12] Hochreiter, S., Schmidhuber, J., 1997. Long short-term memory. Neural Comput. 9(8), 1735-1780.
[13] Hu, X., Wu, S., Zhang, G., Zheng, W., Liu, C., He, C., Zhang, H., 2021. Landslide displacement prediction using kinematics-based random forests method: A case study in Jinping Reservoir Area, China. Eng. Geol. 283, 105975.
[14] Huang, F., Huang, J., Jiang, S., Zhou, C., 2017. Landslide displacement prediction based on multivariate chaotic model and extreme learning machine. Eng. Geol. 218, 173-186.
[15] Huang, F., Zhang, J., Zhou, C., Wang, Y., Huang, J., Zhu, L., 2020a. A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction. Landslides 17, 217-229.
[16] Huang, F., Cao, Z., Guo, J., Jiang, S. H., Li, S., Guo, Z., 2020b. Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. Catena 191, 104580.
[17] Huang, F., Cao, Z., Jiang, S. H., Zhou, C., Huang, J., Guo, Z., 2020c. Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model. Landslides 17, 2919-2930.
[18] Huang, J., Li, Q., Ju, N., Xu, Q., Wang, C., 2019. Displacement prediction of translational landslide based on analysis of major factors and GM-IAGA-WNN model——a case study of Kualiangzi landslide. Journal of Engineering Geology 27(4), 862-872 (in Chinese with English abstract).
[19] Ji, S., Yu, D., Shen, C., Li, W., Xu, Q., 2020. Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks. Landslides 17, 1337-1352.
[20] Jiang, Y., Luo, H., Xu, Q., Lu, Z., Liao, L., Li, H., Hao, L., 2022. A graph convolutional incorporating GRU network for landslide displacement forecasting based on spatiotemporal analysis of GNSS observations. Remote Sens. 14(4), 1016.
[21] Kromer, R.A., Hutchinson, D.J., Lato, M.J., Gauthier, D., Edwards, T., 2015. Identifying rock slope failure precursors using LiDAR for transportation corridor hazard management. Eng. Geol. 195, 93-103.
[22] Kuang, P., Li, R., Huang, Y., Wu, J., Luo, X., Zhou, F., 2022 Landslide displacement prediction via attentive graph neural network. Remote Sens. 14(8), 1919.
[23] Lang, X., Li, W., Zhang, Y., Li, J., Ma, H., 2020. Accuracy detection of Satellite Technology in the Deformation Monitoring of Slope. In: IOP Conference Series: Earth and Environmental Science. IOP Publishing 580(1), 012068.
[24] Li, Y., Yu, R., Shahabi, C., Liu, Y., 2017. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926, doi: 10.48550/arXiv.1707.01926.
[25] Li, J., Song, Z., Hou, S., 2020. Application of Beidou high-precision positioning technology in slope deformation monitoring. The Chinese Journal of Geological Hazard and Control 31(1), 70-78 (in Chinese with English abstract).
[26] Liu, C., 2021. Three types of displacement time curves and early warning of landslides. Journal of Engineering Geology 29(1), 86-95 (in Chinese with English abstract).
[27] Liu, H., He, Y., Miao, Y., 2016. The Slope Stability Monitoring Technology Based on the Beidou/GPS. Journal of Yunnan University 38(S1), 40-43 (in Chinese with English abstract).
[28] Liu, S., Wang, L., Zhang, W., Sun, W., Fu, J., Xiao, T., Dai, Z., 2023. A physics-informed data-driven model for landslide susceptibility assessment in the Three Gorges Reservoir Area. Geosci. Front. 14, 101621.
[29] Ma, Z., Mei, G., Prezioso, E., Zhang, Z., Xu, N., 2021. A deep learning approach using graph convolutional networks for slope deformation prediction based on time-series displacement data. Neural Comput. Appl. 33(21), 14441-14457.
[30] Mallick, T., Balaprakash, P., Rask, E., Macfarlane, J., 2020. Graph-partitioning-based diffusion convolutional recurrent neural network for large-scale traffic forecasting. Transp. Res. Rec. 2674(9), 473-488.
[31] Miao, F., Wu, Y., Xie, Y., Li, Y., 2018. Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model. Landslides 15, 475-488.
[32] Phoon, K.K., Zhang, W.G., 2022. Future of machine learning in geotechnics. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 17, 7-22.
[33] Saito, M., 1969. Forecasting time of slope failure by tertiary creep, in: Proceedings of the 7th International Conference on Soil Mechanics and Foundation Engineering, Mexico City, Mexico, 677-683.
[34] Shang, C., Chen, J., Bi, J., 2021. Discrete graph structure learning for forecasting multiple time series. arXiv preprint arXiv:2101.06861.
[35] Vazquez-Ontiveros, J.R., Vazquez-Becerra, G.E., Quintana, J.A., Carrion, F.J., Guzman-Acevedo, G.M., Gaxiola-Camacho, J.R., 2021. Implementation of PPP-GNSS measurement technology in the probabilistic SHM of bridge structures. Measurement 173, 108677.
[36] Wang, L., Wu, C.Z., Yang, Z.Y., Wang, L.Q., 2023. Deep learning methods for time-dependent reliability analysis of reservoir slopes in spatially variable soils. Comput. Geotech. 159, 105413.
[37] Wu, C., Hong, L., Wang, L., Zhang, R., Pijush, S., Zhang, W., 2022. Prediction of wall deflection induced by braced excavation in spatially variable soils via convolutional neural network. Gondwana Res, https://doi.org/10.1016/j.gr.2022.06.011.
[38] Wu, H., Jiang, Y., 2014. Dam deformation monitoring based on high precision Beidou positioning. Microcontrollers & Embedded Systems 14(1), 76-79.
[39] Xu, Q., 2020. Understanding the landslide monitoring and early warning: Consideration to practical issues. J. Eng. Geol. 28 (2), 360-374.
[40] Xu, Q., Dong, X., Li, W., 2019. Integrated space-air-ground early detection, monitoring and warning system for potential catastrophic geohazards. Geomatics and Information Science of Wuhan University 44(7), 957-966 (in Chinese with English abstract).
[41] Xu, J., Jiang, Y., Yang, C., 2022. Landslide displacement prediction during the sliding process using XGBoost, SVR and RNNs. Applied Sciences 12(12), 6056.
[42] Xu, S., Niu, R., 2018. Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China. Computers & Geosciences 111, 87-96.
[43] Yang, C., Lin, R., Ji, J., Zhang, J., Ding, P., Liu, J., 2022. Slope displacement prediction research based on the graph deep learning and Beidou monitoring. Journal of Engineering Geology, https://doi.org/10.13544/j.cnki.jeg.2022-0053 (in Chinese with English abstract).
[44] Yang, Z., Wang, L., Shi, L., 2020. Research of monitoring and early warning methods for rainfall-induced landslides based on multivariate thresholds. Chin. J. Rock Mech. Eng. 39(2), 272-285.
[45] Yang, B., Yin, K., Lacasse, S., Liu, Z., 2019. Time series analysis and long short-term memory neural network to predict landslide displacement. Landslides 16, 677-694.
[46] Yang, Y., Zheng, Y., Yu, W., 2019. Deformation monitoring using GNSS-R technology. Adv. Space Res. 63(10), 3303-3314.
[47] Yao, W., Zeng, Z., Lian, C., Tang, H., 2015. Training enhanced reservoir computing predictor for landslide displacement. Eng. Geol. 188, 101-109.
[48] Yin, Y., Wang, H., Gao, Y., Li, X., 2010. Real-time monitoring and early warning of landslides at relocated Wushan Town, the Three Gorges Reservoir, China. Landslides 7, 339-349.
[49] Yu, B., Yin, H., Zhu, Z., 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875.
[50] Zhang, W.G., Zhang, R., Wu, C., Goh, A.T.C., Lacasse, S., Liu, Z., Liu, H., 2020. State-of-the-art review of soft computing applications in underground excavations. Geosci. Front. 11(4), 1095-1106.
[51] Zhang, W.G., Wu, C., Zhong, H., Li, Y., Wang, L., 2021. Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geosci. Front. 12(1), 469-477.
[52] Zhang, W.G., Li, H., Han, L., Chen, L., Wang, L., 2022. Slope stability prediction using ensemble learning techniques: a case study in Yunyang County, Chongqing, China. J. Rock Mech. Geotech. Eng. 14(4), 1089-1099.
[53] Zhang, W.G., Wu, C.Z., Tang, L.B., Gu, X., Wang, L., 2022. Efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms. Gondwana Res. https://doi.org/10.1016/j.gr.2022.10.004.
[54] Zhang, W.G., Pradhan, B., Stuyts, B., Xu, C., 2023. Application of artificial intelligence in geotechnical and geohazard investigations. Geological J. https://doi.org/10.1002/gj.4779.
[55] Zhang, W.G., Liu, Z., Rezania, M., 2023. Preface: Advances in data-driven models in geosciences. Gondwana Res. https://doi.org/10.1016/j.gr.2023.06.011.
[56] Zhang, L., Shi, B., Zhu, H., Yu, X. B., Han, H., Fan, X., 2021. PSO-SVM-based deep displacement prediction of Majiagou landslide considering the deformation hysteresis effect. Landslides 18, 179-193.
[57] Zhao, T., Liu, G., Günnemann, S., Jiang, M., 2022. Graph data augmentation for graph machine learning: a survey. arXiv preprint arXiv:2202.08871.
[58] Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Li, H., 2019. T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems 21(9), 3848-3858.
[59] Zhou, F., Li, R., Trajcevski, G., Zhang, K., 2021. Land deformation prediction via slope-aware graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence 35(17), 15033-15040.
[60] Zhou, X., Wen, H., Zhang, Y., Xu, J., Zhang, W., 2021. Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization. Geosci. Front. 12(5), 101211.
[61] Zhou, C., Yin, K., Cao, Y., Ahmed, B., 2016. Application of time series analysis and PSO-SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China. Eng. Geol. 204, 108-120.
[62] Zhou, C., Yin, K., Cao, Y., Intrieri, E., Ahmed, B., Catani, F., 2018. Displacement prediction of step-like landslide by applying a novel kernel extreme learning machine method. Landslides 15, 2211-2225.
-
期刊类型引用(15)
1. Shabarov, A.N., Kuzin, A.A., Filippov, V.G. Surveying procedure for slope landslide using satellite-based measurements | [МЕТОДИКА МАРКШЕЙДЕРСКО-ГЕОДЕЗИЧЕСКИХ НАБЛЮДЕНИЙ ОПОЛЗНЕВОГО ПРОЦЕССА СКЛОНОВОЙ СИСТЕМЫ НА ОСНОВЕ ДАННЫХ СПУТНИКОВЫХ ОПРЕДЕЛЕНИЙ]. Mining Informational and Analytical Bulletin, 2025. 
必应学术
2. Zai, D., Pang, R., Xu, B. et al. A novel data-driven hybrid intelligent prediction model for reservoir landslide displacement. Bulletin of Engineering Geology and the Environment, 2024, 83(12): 493. 必应学术
3. Li, K.-Q., He, H.-L. Towards an improved prediction of soil-freezing characteristic curve based on extreme gradient boosting model. Geoscience Frontiers, 2024, 15(6): 101898. 必应学术
4. He, R., Zhang, W., Dou, J. et al. Application of artificial intelligence in three aspects of landslide risk assessment: A comprehensive review. Rock Mechanics Bulletin, 2024, 3(4): 100144. 必应学术
5. Kuzin, A.A., Filippov, V.G. Method for determining the plan view coordinates and height of the working benchmark on a landslide with forced inclinations of the pole from the plumb position. Geodeziya i Kartografiya, 2024, 2024(9): 2-11. 必应学术
6. Wu, M., Xu, X., Han, X. et al. Seismic performance prediction of a slope-pile-anchor coupled reinforcement system using recurrent neural networks. Engineering Geology, 2024, 338: 107623. 必应学术
7. Yang, J., Huang, Z., Jian, W. et al. Landslide displacement prediction by using Bayesian optimization–temporal convolutional networks. Acta Geotechnica, 2024, 19(7): 4947-4965. 必应学术
8. Li, X., Huang, F., Yang, Z. Multisource monitoring data-driven slope stability prediction using ensemble learning techniques. Computers and Geotechnics, 2024, 169: 106255. 必应学术
9. Wang, H., Shao, P., Wang, H. et al. A VMD-DES-TSAM-LSTM-based interpretability multi-step prediction approach for landslide displacement. Environmental Earth Sciences, 2024, 83(7): 193. 必应学术
10. Ma, K., He, D., Liu, S. et al. Novel time-lag informed deep learning framework for enhanced streamflow prediction and flood early warning in large-scale catchments. Journal of Hydrology, 2024, 631: 130841. 必应学术
11. Catani, F., Nava, L., Bhuyan, K. Artificial intelligence applications for landslide mapping and monitoring on EO data. Earth Observation Applications to Landslide Mapping, Monitoring and Modeling: Cutting-edge Approaches with Artificial Intelligence, Aerial and Satellite Imagery, 2024. 必应学术
12. Kaushal, A., Kumar Gupta, A., Sehgal, V.K. A Data-Driven and Hybrid Approach for Real-Time Threshold-Based Landslide Prediction in Hilly Regions using Sensor Networks. Remote Sensing Letters, 2024, 15(12): 1218-1228. 必应学术
13. Yang, C., Zhu, Y., Zhang, J. et al. A feature fusion method on landslide identification in remote sensing with Segment Anything Model. Landslides, 2024. 必应学术
14. Kazemi Garajeh, M., Guariglia, A., Paridad, P. et al. Detecting small-scale landslides along electrical lines using robust satellite-based techniques. Geomatics, Natural Hazards and Risk, 2024, 15(1): 2409203. 必应学术
15. Alvarado Jimenez, C.A., Diaz Amaya, E.D., Lo Coronado, L.J. Predictive analysis model to define behavioral patterns of landslide for early warning based on machine learning and data from hydrological and meteorological sensors in Chosica. Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology, 2024. 必应学术
其他类型引用(1)
-
其他相关附件
计量
- 文章访问数: 203
- HTML全文浏览量: 1
- PDF下载量: 1
- 被引次数: 16
下载:
