Application of dynamic optimization time-delay GM(1,2) model in landslide displacement prediction considering the influence of rainfall

doi:10.11947/j.AGCS.2022.20220290

Abstract

Abstract: In addition to the displacement caused by its own gravity, the landslide body is also affected by rainfall, but usually the effect of rainfall on the displacement of the landslide has a hysteresis. In order to analyze and predict the impact of rainfall on landslide displacement, this paper proposes a dynamic optimization time-lag time-lag GM(1,2) landslide displacement prediction model that takes into account the impact of rainfall. First, use EMD (empirical mode decomposition) to decompose the displacement sequence and reconstruct the periodic displacement sequence and the trend displacement sequence through the time sequence. Perform time lag analysis and correlation analysis on the rainfall data and the landslide periodic displacement sequence, determine the time lag and the degree of influence, and establish an optimization based on the background value. The dynamic time-lag GM(1,2) model predicts the cyclic displacement change of the landslide caused by the change of rainfall. At the same time, a threshold autoregressive model is established to predict the trend displacement of the landslide tending to natural changes. Finally, the landslide prediction displacement taking into account the influence of rainfall is obtained through time series superposition. Established a dynamic optimization time lag time GM(1,2) combined forecasting method that takes into account the rainfall factor. The paper uses the monitoring data of Funing Bachimen landslide and Zigui county Bazimen landslide as examples to verify the accuracy of the dynamic optimization time-lag GM(1,2) model, and compares and analyzes the prediction results with other models. The experimental results show that the dynamic the optimized time-lag time series GM(1,2) combined forecasting model can accurately predict the landslide displacement changes caused by rainfall, and the forecasting effect is better, the combined model has certain reference value for the early warning and prevention of landslide disasters.

Key words: prediction of landslide displacement, rainfall, time series, correlation analysis, dynamic optimization time-delay GM(1,2) model, threshold autoregressive model

CLC Number:

P642

GAO Yaping, CHEN Xi, TU Rui. Application of dynamic optimization time-delay GM(1,2) model in landslide displacement prediction considering the influence of rainfall[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2183-2195.

References

[1] 张勤,白正伟,黄观文,等. GNSS滑坡监测预警技术进展[J].测绘学报, 2022, 51(10): 1985-2000. DOI: 10.11947/j.AGCS.2022.20220299. ZHANG Qin,BAI Zhengwei,HUANG Guanwen,et al.Progress of landslide monitoring and early warning technology by GNSS [J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 1985-2000. DOI: 10.11947/j.AGCS.2022.20220299.
[2] 刘青豪, 张永红, 邓敏, 等. 大范围地表沉降时序深度学习预测法[J]. 测绘学报, 2021, 50(3): 396-404. DOI: 10.11947/j.AGCS.2021.20200038. LIU Qinghao, ZHANG Yonghong, DENG Min, et al. Time series prediction method of large-scale surface subsidence based on deep learning[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(3): 396-404. DOI:10.11947/j.AGCS.2021.20200038.
[3] 许强, 朱星, 李为乐, 等. “天-空-地”协同滑坡监测技术进展[J]. 测绘学报,2022,51(7):1416-1436. DOI: 10.11947/j.AGCS.2022.20220320. XU Qiang, ZHU Xing, LI Weile, et al. Technical progress of space-air-ground collaborative monitoring of landslide[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(7): 1416-1436. DOI: 10.11947/j.AGCS.2022.20220320.
[4] 白正伟, 张勤, 黄观文, 等. “轻终端+行业云”的实时北斗滑坡监测技术[J]. 测绘学报,2019,48(11):1424-1429. DOI: 10.11947/j.AGCS.2019.20190167. BAI Zhengwei, ZHANG Qin, HUANG Guanwen, et al. Real-time BeiDou landslide monitoring technology of “light terminal plus industry cloud”[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(11): 1424-1429. DOI: 10.11947/j.AGCS.2019.20190167.
[5] 李小伟, 王琳, 王力, 等. 阶跃型滑坡的位移预测方法研究——以三峡库区树坪滑坡为例[J]. 三峡大学学报(自然科学版), 2021, 43(1): 38-42. LI Xiaowei, WANG Lin, WANG Li, et al. Research on displacement prediction method of step type landslide—taking shuping landslide in Three Gorges Reservoir area as an example[J]. Journal of China Three Gorges University (Natural Sciences), 2021, 43(1): 38-42.
[6] WANG Jing, NIE Guigen, GAO Shengjun, et al. Landslide deformation prediction based on a GNSS time series analysis and recurrent neural network model[J]. Remote Sensing, 2021, 13(6), 1055.
[7] KRKAČ M, BERNAT GAZIBARA S, ARBANAS Ž, et al. A comparative study of random forests and multiple linear regression in the prediction of landslide velocity[J]. Landslides, 2020, 17(11): 2515-2531.
[8] YANG Beibei, YIN Kunlong, LACASSE Suzanne, et al. Time series analysis and long short-term memory neural network to predict landslide displacement[J]. Landslides, 2019, 16(4): 677-694.
[9] XING Yin, YUE Jianping, CHEN Chuang. Interval estimation of landslide displacement prediction based on time series decomposition and long short-term memory network[J]. IEEE ACCESS, 2020(8): 3187-3196.
[10] XING Yin, YUE Jianping, CHEN Chuang, et al. Prediction interval estimation of landslide displacement using adaptive chicken swarm optimization-tuned support vector machines[J]. Applied Intelligence, 2021, 51(11): 8466-8483.
[11] LU Xuesong, MIAO Fasheng, XIE Xiaoxu, et al. A new method for displacement prediction of “step-like” landslides based on VMD-FOA-SVR model[J]. Environmental Earth Sciences, 2021, 80(17): 1-12.
[12] SHIHABUDHEEN K V, PILLAI G N, PEETHAMBARAN Bipin. Prediction of landslide displacement with controlling factors using extreme learning adaptive neuro-fuzzy inference system (ELANFIS)[J]. Applied Soft Computing, 2017, 61: 892-904.
[13] LI S H, WU L Z, CHEN J J, et al. Multiple data-driven approach for predicting landslide deformation[J]. Landslides, 2020, 17(3): 709-718.
[14] 潘国荣, 乔立洋, 王穗辉. 半参数改进灰色模型在滑坡变形预测中的应用[J]. 测绘科学, 2019, 44(09): 164-170. PAN Guorong, QIAO Liyang, WANG Suihui. Application of improved semiparametric gray model in landslide deformation prediction [J]. Science of Surveying and Mapping, 2019, 44(9): 164-170.
[15] 黄健, 李桥, 巨能攀, 等. 基于主控因子分析与GM-IAGA-WNN联合模型的平推式滑坡位移预测研究——以垮梁子滑坡为例[J]. 工程地质学报, 2019, 27(4): 862-872. HUANG Jian, LI Qiao, JU Nengpan, et al. Displacement prediction of translational landslide based on anslysis of major factors and GM-IAGA-WNN Model—a case study of Kualiangzi landslide [J]. Journal of Engineering Geology, 2019, 27(4): 862-872.
[16] 李秀珍, 孔纪名, 王成华. 灰色GM(1, 1)残差修正模型在滑坡预测中的对比应用[J]. 山地学报, 2007, 25(6): 741-746. LI Xiuzhen, KONG Jiming, WANG Chenghua. Comparison and application of grey GM( 1,1 ) modified residual error models in landslide predicting [J]. Journal of Mountain Science, 2007, 25(6): 741-746.
[17] 李秀珍, 孔纪名, 王成华. 中心逼近式灰色GM(1, 1)模型在滑坡变形预测中的应用[J]. 工程地质学报, 2007,15(5): 673-676. LI Xiuzhen, KONG Jiming, WANG Chenghua. Application of center approach grey GM (1,1) model to prediction of landslide deformation with a case study[J]. Journal of Engineering Geology, 2007,15(5): 673-676.
[18] 王高峰, 孙秀娟, 孙向东, 等. 动态多变量灰色模型在危岩变形预测中的应用[J]. 河海大学学报(自然科学版), 2014, 42(6): 508-512. WANG Gaofeng, SUN Xiujuan, SUN Xiangdong, et al. Application of dynamic multi-variable grey model to predication of deformation of unstable rock [J]. Journal of Hohai University (Natural Sciences), 2014, 42(6): 508-512.
[19] 杨何,汤明高,许强, 等.长江三峡库区滑坡变形统计特征研究[J].灾害学, 2021, 36(2): 37-42. YANG He, TANG Minggao, XU Qiang, et al. Research of statistical characteristics of deformation of landslides in the Three Gorges Reservoir area of the Yangtze River[J]. Journal of Catastrophology, 2021, 36(2): 37-42.
[20] 李俊, 康馨予. 降雨诱发滑坡的流-固耦合特征及稳定性分析[J]. 防灾减灾学报, 2020, 36(4): 37-44. LI Jun, KANG Xinyu. Flow-solid coupling characteristics and stability analysis of rainfall-induced landslides[J]. Journal of Disaster Prevention and Reduction, 2020, 36(4): 37-44.
[21] 邓茂林, 易庆林, 卢书强, 等. 三峡库区靠椅状土质滑坡变形规律及机理——以秭归八字门滑坡为例[J]. 南水北调与水利科技(中英文), 2020, 18(2): 135-143,151. DENG Maolin, YI Qinglin, LU Shuqiang, et al. Study on the deformation law and mechanism of chair-shaped soil landslide in Three Gorges Reservoir area—taking the landslide of Baguamen in Zigui as an example[J]. South-to-North Water Transfers and Water Science& Technology, 2020, 18(2): 135-143,151.
[22] 黄观文, 王家兴, 杜源, 等. 顾及降雨及库水位因素的滑坡时滞分析与预测——以三峡库区新铺滑坡为例[J]. 地球科学与环境学报, 2021, 43(3): 621-631. HUANG Guanwen, WANG Jiaxing, DU Yuan, et al. Time-delay analysis and prediction of landslide considering precipitation and reservoir water level—a case study of xinpu landslide in Three Gorges reservoir area, China[J]. Journal of Earth Sciences and Environment, 2021, 43(3): 621-631.
[23] 王朝阳, 许强, 范宣梅, 等. 灰色新陈代谢GM(1, 1)模型在滑坡变形预测中的应用[J]. 水文地质工程地质, 2009, 36(2): 108-111. WANG Zhaoyang, XU Qiang, FAN Xuanmei, et al. Application of renewal gray GM(1, 1) model to prediction of landslide deformation with two case studies[J]. Hydrogeology& Engineering Geology, 2009, 36(2): 108-111.
[24] 李世贵, 易庆林, 吴娟娟, 等. 背景值优化的多点灰色模型在滑坡变形预测中的应用[J]. 中国地质灾害与防治学报, 2015, 26(2): 3-9. LI Shigui, YI Qinglin, WU Juanjuan, et al. A multi-point model for background value optimization and its application to landslide deformation prediction[J]. The Chinese Journal of Geological Hazard and Control, 2015, 26(2): 3-9.
[25] CHAN K S. Percentage points of likelihood ratio tests for threshold autoregression[J]. Journal of the Royal Statistical Society: Series B (Methodological), 1991, 53(3): 691-696.
[26] MOTEGI Kaiji, CAI Xiaojing, HAMORI Shigeyuki, et al. Moving average threshold heterogeneous autoregressive (MAT-HAR) models[J]. Journal of Forecasting, 2020, 39(7): 1035-1042.
[27] HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings Mathematical Physical & Engineering Sciences, 1998, 454(1971):903-995.
[28] REN Y, SUGANTHAN P N, SRIKANTH N. A comparative study of empirical mode decomposition-based short-term wind speed forecasting methods[J]. IEEE Transactions on Sustainable Energy, 2017, 6(1): 236-244.
[29] MANDIC Danilo P, REHMAN Naveed U, WU Zhaohua, et al. Empirical mode decomposition-based time-frequency analysis of multivariate signals[J]. IEEE Signal Processing Magazine, 2013, 30(6): 74-86.
[30] 邓聚龙. 灰理论基础[M]. 武汉: 华中科技大学出版社, 2002. DENG Julong. The basis of grey theory [M]. Wuhan: Huazhong University of Science and Technology Press, 2002.
[31] 邓聚龙. 灰色系统理论教程[M]. 武汉: 华中理工大学出版社, 1990. DENG Julong. Gray System Theory Course[M]. Wuhan: Huazhong University of Science and Technology Press, 1990.
[32] DAI Jin, Liu Huijie, SUN Yannan, et al. An optimization method of multi-variable MGM (1, m) prediction model's background value[J]. Journal of Grey System, 2018, 30(1): 221-238.
[33] MA Xin, XIE Mei, Wu Wenqing, et al. The novel fractional discrete multivariate grey system model and its applications[J]. Applied Mathematical Modelling, 2019, 70: 402-424.
[34] LUO Youxin, WU Xiao, LI Min, et al. Grey dynamic model GM(1,N) for the relationship of cost and variability[J]. Journal of Textile Research, 2003, 38(3-4): 435-440.
[35] KHAN M Y, MITTNIK S. Nonlinear time series modeling and forecasting the seismic data of the Hindu Kush region[J]. Journal of Seismology, 2018, 22(1): 353-376.
[36] LIM KS. An algorithm for determining the chaos of TAR(1) [J]. Communications in Statistics-simulation and Computation, 1995, 24(2): 505-522.
[37] 张明明. 2000—2015年中国干旱半干旱区蒸散发时空变化及其影响因素分析[D]. 西安: 长安大学, 2019. ZHANG Mingming. Analysis of the temporal and spatial variation of evapotranspiration and its driving factors in arid and semi-arid region of China from 2000 to 2015[D]. Xi'an: Changan University, 2019.