ПРОГНОЗ СМЕЩЕНИЯ ОПОЛЗНЕЙ НА ОСНОВЕ ГИБРИДНОЙ МОДЕЛИ VMD–TCN Landslide Displacement Prediction Based on VMD-TCN Hybrid Model

Chunying Xu, Kaibin Huang, Chuliang Wei, Xinjie Wu, Ruixing Liang, Yifei Dong

Аннотация


Предложена гибридная модель, основанная на декомпозиции с переменным режимом (VMD) и временных сетях (TCN) для прогноза смещения оползней. Метод VMD используется для декомпозиции временных рядов данных о совокупном перемещении оползней и факторах воздействия на окружающую среду (например, уровень водохранилища и количество осадков), а модель TCN использовалась для составления прогнозов. Экспериментальные результаты показывают, что предлагаемая в данной работе модель VMD-TCN обладает более высокой точностью, чем другие модели, что повышает надежность прогнозирования и позволяет избежать проблемы исчезновения градиента в моделях LSTM. Представлено практическое применение теории на примере оползня Байшуйхэ. Модель VMD–TCN может играть важную роль в прогнозировании смещения оползней и служить ориентиром для раннего предупреждения деформации при смещении оползней.


Полный текст статьи публикуется в английской версии журнала
«Soil Mechanics and Foundation Engineering”, vol.62, No.3


Литература


Zhu, X., Xu, Q., Tang, M., Nie, W., Ma, S., Xu, Z.: Comparison of two optimized machine learning models for predicting displacement of rainfall-induced landslide: A case study in sichuan province, china. Engineering geology 218, 213–222 (2017)

Segoni, S., Piciullo, L., Gariano, S.L.: Preface: landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception. Natural Hazards and Earth System Sciences 18(12), 3179–3186 (2018)

Froude, M.J., Petley, D.N.: Global fatal landslide occurrence from 2004 to 2016. Natural Hazards and Earth System Sciences 18(8), 2161–2181 (2018)

Lian, C., Zeng, Z., Yao, W., Tang, H.: Multiple neural networks switched prediction for landslide displacement. Engineering geology 186, 91–99 (2015)

Miao, F., Xie, X., Wu, Y., Zhao, F.: Data mining and deep learning for predicting the displacement of “step-like” landslides. Sensors 22(2), 481 (2022)

Chae, B.-G., Park, H.-J., Catani, F., Simoni, A., Berti, M.: Landslide prediction, monitoring and early warning: a concise review of state-of-the-art. Geosciences Journal 21(6), 1033–1070 (2017)

Zhao, N., Hu, B., Yan, E., Xu, X., Yi, Q.: Research on the creep mechanism of huangniba landslide in the three gorges reservoir area of china considering the seepage–stress coupling effect. Bulletin of Engineering Geology and the Environment 78(6), 4107–4121 (2019)

Lee, C.-H., Huang, Z.: Effects of grain size on subaerial granular landslides and resulting impulse waves: experiment and multiphase flow simulation. Landslides 19(1), 137–153 (2022)

Takabatake, T., Han, D.C., Valdez, J.J., Inagaki, N., M¨all, M., Esteban, M., Shibayama, T.: Three-dimensional physical modeling of tsunamis generated by partially submerged landslides. Journal of Geophysical Research: Oceans 127(1), 2021–017826 (2022)

Zhang, Y., Hu, X., Tannant, D.D., Zhang, G., Tan, F.: Field monitoring and deformation characteristics of a landslide with piles in the three gorges reservoir area. Landslides 15(3), 581–592 (2018)

Gorsevski, P.V., Gessler, P.E., Foltz, R.B., Elliot, W.J.: Spatial prediction of landslide hazard using logistic regression and roc analysis. Transactions in GIS 10(3), 395–415 (2006)

Krkaˇc, M., Bernat Gazibara, S., Arbanas, Zˇ., Seˇcanj, M., Mihali´c Arbanas, S.: A comparative study of random forests and multiple linear regression in the prediction of landslide velocity. Landslides 17(11), 2515–2531 (2020)

Cao, Y., Yin, K., Alexander, D.E., Zhou, C.: Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors. Landslides 13(4), 725–736 (2016)

Li, X., Kong, J.: Application of ga-svm method with parameter optimization for landslide development prediction. Natural Hazards and Earth System Sciences 14(3), 525–533 (2014)

Lu, X., Miao, F., Xie, X., Li, D., Xie, Y.: A new method for displacement prediction of “step-like” landslides based on vmd-foa-svr model. Environmental Earth Sciences 80(17), 1–12 (2021)

Zhao, J., Liu, Y., Liu, N., Hu, M.: Spatial prediction method of regional landslide based on distributed bp neural network algorithm under massive monitoring data. j. Rock and Soil Mechanics 7, 1–8 (2019)

Yang, B., Yin, K., Lacasse, S., Liu, Z.: Time series analysis and long short-term memory neural network to predict landslide displacement. Landslides 16(4), 677– 694 (2019)

Wang, J., Jiang, W., Li, Z., Lu, Y.: A new multi-scale sliding window lstm frame- work (mssw-lstm): A case study for gnss time-series prediction. Remote Sensing 13(16), 3328 (2021)

Taorui, Z., Hongwei, J., Qingli, L., Kunlong, Y.: Landslide displacement pre- diction based on variational mode decomposition and mic-gwo-lstm model. Stochastic Environmental Research and Risk Assessment, 1–20 (2022)

Gao, Y., Chen, X., Tu, R., Chen, G., Luo, T., Xue, D.: Prediction of landslide displacement based on the combined vmd-stacked lstm-tar model. Remote Sensing 14(5), 1164 (2022)

Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE transactions on signal processing 62(3), 531–544 (2013)

Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)

Qin, S., Jiao, J.J., Wang, S.: A nonlinear dynamical model of landslide evolution. Geomorphology 43(1-2), 77–85 (2002)

Lea, C., Flynn, M.D., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks for action segmentation and detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 156–165 (2017)

Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

Salimans, T., Kingma, D.P.: Weight normalization: A simple reparameterization to accelerate training of deep neural networks. Advances in neural information processing systems 29 (2016)

Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Icml (2010)

Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15(1), 1929–1958 (2014)

National Cryosphere Desert Data Center: Basic Characteristics and Monitoring Data of Baishuihe Landslide in Zigui County, Three Gorges Reservoir Area of Yangtze River from 2007 to 2012. http://www.ncdc.ac.cn (2012)


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