一种基于多测点时空模态信息互馈的阶跃式滑坡位移预测方法

doi:10.3724/1000-6915.jrme.2025.0013

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (1876 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract This study proposes a refined prediction method that leverages mutual feedback of spatiotemporal modal information from multiple monitoring points. This approach addresses the limitations of traditional step-like landslide displacement prediction methods regarding granularity and timeliness at annual and monthly scales, as well as the insufficient accuracy arising from increased nonlinearity and redundant information interference at the daily scale. Initially, the variational modal decomposition method, optimized using the sparrow search algorithm (SSA-VMD), is employed to minimize the system?s redundant information entropy. The displacement sequence is decomposed into trend term displacements that reflect material properties, along with periodic and random term displacements influenced by environmental factors. Subsequently, Pearson correlation analysis and Granger causality analysis are performed to identify significant statistical associations between the displacement modes and ecological factors, as well as to filter relevant statistical relationships between displacement modes and environmental variables. This is followed by the design of a CNN-Informer deep learning structure. Spatiotemporal information is extracted using convolutional neural networks (CNN), while the sparse attention mechanism and self-attention distillation mechanism of the informer are utilized to mitigate interference from redundant information and dynamically capture long-term dependency relationships between features and displacements. The ultimate outcome is a point-level daily displacement forecast achieved through time-series reconstruction. This study utilizes daily monitoring data from the monitoring points GSCX3, ZGX111, and GSCX5, collected from October 2017 to December 2021, to analyze the step-like Bazimen landslide. The results indicate that: (1) The model achieves RMSE values of 2.61, 3.84, and 3.91 mm for the three monitoring points, demonstrating a 71.94% reduction in RMSE at GSCX3 compared to alternative models, effectively predicting daily step-like displacement evolution patterns; (2) In the presence of noise interference, significant data absence, and limited samples, the maximum RMSE and MAE reach 6.10 mm and 4.17 mm, respectively, showcasing substantial robustness; (3) Sensitivity analysis reveals a 313.79% increase in RMSE for single-point predictions without mutual feedback, underscoring the critical role of multi-monitoring point spatiotemporal information mutual feedback in enhancing displacement predictions for step-like landslides.

Key words： slope engineering step-like landslide refined displacement prediction modal decomposition spatiotemporal characteristics information mutual feedback deep learning

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	GAO Zhenwu1
	XU Lifu1
	2
	ZHU Yantao1
	3
	GUO Fei4
	5
	YAN Tianyou2
	YI Qinglin4

Cite this article:

GAO Zhenwu1,XU Lifu1,2, et al. A step-like landslide displacement prediction method based on mutual feedback of spatio-temporal modal information from multi-monitoring point[J]. , 2025, 44(8): 2122-2138.

URL:

https://rockmech.whrsm.ac.cn/EN/10.3724/1000-6915.jrme.2025.0013 OR https://rockmech.whrsm.ac.cn/EN/Y2025/V44/I8/2122

[1]	贲琰棋，易武，李华兵，等. 基于“阶跃”变形特征的降雨型滑坡预警判据探讨[J]. 中国地质灾害与防治学报，2023，34(4)：30-38.(BEN Yanqi，YI Wu，LI Huabing，et al. Preliminary analysis on rainfall thresholds for early warning of the rainfall induced landslides based on“step”deformation characteristics[J]. The Chinese Journal of Geological Hazard and Control，2023，34(4)：30-38.(in Chinese))
[2]	袁维，孙瑞峰，钟辉亚，等. 阶跃型滑坡综合变形预测及监测预警方法研究[J]. 水利学报，2023，54(4)：461-473.(YUAN Wei，SUN Ruifeng，ZHONG Huiya，et al. Research on comprehensive deformation prediction and monitoring and early warning method for step-like landslide[J]. Journal of Hydraulic Engineering，2023，54(4)：461-473.(in Chinese))
[3]	饶云康，富海鹰，杨涛. 堆积层滑坡偏角比预警模型[J]. 岩石力学与工程学报，2024，43(10)：2 540-2 551.(RAO Yunkang，FU Haiying，YANG Tao. Early warning model of deflection angel ratio for accumulation landslides[J]. Chinese Journal of Rock Mechanics and Engineering，2024，43(10)：2 540-2 551.(in Chinese))
[4]	SONG K，YANG H，LIANG D，et al. Step-like displacement prediction and failure mechanism analysis of slow-moving reservoir landslide[J]. Journal of Hydrology，2024，628：130588.
[5]	武生辉，仝德富，苏爱军，等. 新铺下二台滑坡变形机制及中长期预报模型[J]. 地质科技通报，2022，41(6)：35-44.(WU Shenghui，TONG Defu，SU Aijun，et al. Deformation mechanism and medium and long-term landslide prediction model of Xinpu Xia?ertai landslide[J]. Bulletin of Geological Science and Technology，2022，41(6)：35-44.(in Chinese))
[6]	XIA D，TANG H，GLADE T. A novel approach to enhance landslide displacement prediction with finer monitoring data：A case study of the Baijiabao Landslide[J]. Remote Sensing，2024，16(4)：618.
[7]	FU Z，LI D Q，WANG S，et al. Causes of episodic movement of the Baijiabao landslide based on multiple-time scale analysis[J]. Landslides，2024，21(5)：1 069-1 082.
[8]	梁宏涛，刘硕，杜军威，等. 深度学习应用于时序预测研究综述[J]. 计算机科学与探索，2023，17(6)：1 285-1 300.(LIANG Hongtao，LIU Shuo，DU Junwei，et al. Review of deep learning applied to time series prediction[J]. Journal of Frontiers of Computer Science and Technology，2023，17(6)：1 285-1 300.(in Chinese))
[9]	罗袆沅，蒋亚楠，许强，等. 基于深度学习的滑坡位移时空预测[J]. 测绘学报，2022，51(10)：2 160-2 170.(LUO Huiyuan，JIANG Ya?nan，XU Qiang，et al. A spatio-temporal network for landslide displacement prediction based on deep learning[J]. Acta Geodaetica et Cartographica Sinica，2022，51(10)：2 160-2 170.(in Chinese))
[10]	XU M，ZHANG D，LI J，et al. An adaptive spatial-temporal prediction model for landslide displacement based on decomposition architecture[J]. Engineering Applications of Artificial Intelligence，2024，137：109215.
[11]	李亚兰，金炜东. 全矢IMF信息熵用于高速列车转向架故障诊断[J]. 振动、测试与诊断，2021，41(5)：874-879.(LI Yalan，JIN Weidong. Application of full vector IMF entropy in fault diagnosis of high speed train[J]. Journal of Vibration，Measurement and Diagnosis，2021，41(5)：874-879.(in Chinese))
[12]	ZHU J，BAI W，ZHAO J，et al. Variational mode decomposition and sample entropy optimization based transformer framework for cloud resource load prediction[J]. Knowledge-Based Systems，2023，280：111042.
[13]	DU H，SONG D，CHEN Z，et al. Prediction model oriented for landslide displacement with step-like curve by applying ensemble empirical mode decomposition and the PSO-ELM method[J]. Journal of Cleaner Production，2020，270：122248.
[14]	MENG S，SHI Z，PENG M，et al. Landslide displacement prediction with step-like curve based on convolutional neural network coupled with bi-directional gated recurrent unit optimized by attention mechanism[J]. Engineering Applications of Artificial Intelligence，2024，133：108078.
[15]	张振坤，张冬梅，李江，等. 基于多头自注意力机制的LSTM-MH-SA滑坡位移预测模型研究[J]. 岩土力学，2022，43(增2)：477-486.(ZHANG Zhenkun，ZHANG Dongmei，LI Jiang，et al. LSTM-MH-SA landslide displacement prediction model based on multi-head self-attention mechanism[J]. Rock and Soil Mechanics，2022，43(Supp.2)：477-486.(in Chinese))
[16]	王震豪，聂闻，许汉华，等. 基于EEMD-Prophet-LSTM的滑坡位移预测[J]. 中国科学院大学学报，2023，40(4)：514-522. (WANG Zhenhao，NIE Wen，XU Hanhua，et al. Prediction of landslide displacement based on EEMD-Prophet-LSTM[J]. Journal of University of Chinese Academy of Sciences，2023，40(4)：514-522.(in Chinese))
[17]	周超，殷坤龙，黄发明. 混沌序列WA-ELM耦合模型在滑坡位移预测中的应用[J]. 岩土力学，2015，36(9)：2 674-2 680. (ZHOU Chao，YIN Kunlong，HUANG Faming. Application of the chaotic sequence WA-ELM coupling model in landslide displacement prediction[J]. Rock and Soil Mechanics，2015，36(9)：2 674-2 680. (in Chinese))
[18]	WANG Y，TANG H，WEN T，et al. A hybrid intelligent approach for constructing landslide displacement prediction intervals[J]. Applied Soft Computing，2019，81：105506.
[19]	刘伯相，张远民，江竹. 基于变分模态分解的管道泄漏信号降噪方法[J]. 振动、测试与诊断，2023，43(2)：397-403.(LIU Boxiang，ZHANG Yuanmin，JIANG Zhu. Noise reduction method of pipeline leakage signal based on variational mode decomposition[J]. Journal of Vibration，Measurement and Diagnosis，2023，43(2)：397-403.(in Chinese))
[20]	WANG M，WANG W，ZENG J，et al. An integrated method based on sparrow search algorithm improved variational mode decomposition and support vector machine for fault diagnosis of rolling bearing[J]. Journal of Vibration Engineering and Technologies，2022，10(8)： 2 893-2 904.
[21]	郭飞，黄晓虎，邓茂林，等. 三峡库区“阶跃”型滑坡变形机制与预警模型[J]. 测绘学报，2022，51(10)：2 205-2 215.(GUO Fei，HUANG Xiaohu，DENG Maolin，et al. Study on deformation mechanism and warning model of step-like landslide in Three Gorges Reservoir area[J]. Acta Geodaetica et Cartographica Sinica，2022，51(10)：2 205-2 215.(in Chinese))
[22]	熊珅，易武，王力，等. 三峡库区八字门滑坡变形破坏机制分析[J]. 中国地质灾害与防治学报，2019，30(5)：9-18. (XIONG Shen，YI Wu，WANG Li，et al. Analysis of deformation and failure mechanism of Bazimen Landslide in Three Gorges Reservoir Area[J]. The Chinese Journal of Geological Hazard and Control，2019，30(5)：9-18.(in Chinese))
[23]	LI B，WANG G，CHEN L C，et al. Analysis of landslide deformation mechanisms and coupling effects under rainfall and reservoir water level effects[J]. Engineering Geology，2024，343：107803.
[24]	江强强，徐杨青，王浩，等. 干湿循环对滑带土基质吸力与抗剪强度的影响[J]. 地球科学与环境学报，2022，44(6)：959-970. (JIANG Qiangqiang，XU Yangqing，WANG Hao，et al. Effect of drying-wetting cycles on matric suction and shear strength of slip zone soil[J]. Journal of Earth Sciences and Environment，2022，44(6)：959-970.(in Chinese))
[25]	王力，南芳芸，王世梅，等. 三峡库区降雨型滑坡入渗特征及变形机制——基于一维和二维模型试验研究[J]. 岩土力学，2023，44(5)：1 363-1 374.(WANG Li，NAN Fangyun，WANG Shimei，et al. Infiltration characteristics and deformation mechanism of rainfall-induced landslides in Three Gorges Reservoir Area based on 1D and 2D model tests[J]. Rock and Soil Mechanics，2023，44(5)：1 363- 1 374.(in Chinese))
[26]	朱鸿鹄，王佳，李厚芝，等. 基于数据挖掘的三峡库区特大滑坡变形关联规则研究[J]. 工程地质学报，2022，30(5)：1 517- 1 527.(ZHU Honghu，WANG Jia，LI Houzhi，et al. Association rule analysis for giant landslide deformation of the Three Gorges Reservoir Region based on data mining[J]. Journal of Engineering Geology，2022，30(5)：1 517-1 527.(in Chinese))
[27]	WU H，DU P，HENG J. Gated convolution with attention mechanism under variational mode decomposition for daily rainfall forecasting[J]. Measurement，2024，237：115222.
[28]	KAFLE L，XU W J，ZENG S Y，et al. A numerical investigation of slope stability influenced by the combined effects of reservoir water level fluctuations and precipitation：A case study of the Bianjiazhai landslide in China[J]. Engineering Geology，2022，297：106508.