Displacement prediction model of landslide based on ensemble empirical mode decomposition and support vector regression

doi:10.11947/j.AGCS.2022.20220291

Abstract

Abstract: Landslide displacement prediction is an important part of real-time monitoring and early warning of landslide disasters. A good landslide displacement prediction model is helpful to predict the occurrence of geological disasters. The deformation of the landslide is affected by a variety of external factors and presents the characteristics of randomness and nonlinearity. Among the existing landslide displacement prediction methods, machine learning methods have been widely used in landslide displacement prediction. Prediction of landslide displacement is the superposition of trend displacement and periodic displacement. In this study, the displacement extraction method of landslide trend term and period term based on integrated empirical mode decomposition (EEMD) and the support vector regression (SVR) model were used to predict the landslide displacement. The construction process and prediction performance of the model are introduced in detail, and the root mean square error (RMSE), average absolute error (MAE), average absolute percentage error (MAPE) and coefficient of determination (R²) are used as the predictive performance indicators of the evaluation model. The EEMD-SVR, SVR, and Elman models are used to predict the displacement of a landslide in the karst mountainous area of Guizhou province. The results showed that the RMSE, MAPE and R² values of EEMD-SVR model were 0.648 mm, 0.518% and 0.996 8, respectively. This model can provide higher and more reliable landslide displacement prediction accuracy, and has certain reference value for the displacement prediction of similar landslide.

Key words: landslide displacement prediction, timeseries, machine learning, ensemble empirical mode decomposition, support vector regression

CLC Number:

P232

WANG Chenhui, ZHAO Yijiu, GUO Wei, MENG Qingjia, LI Bin. Displacement prediction model of landslide based on ensemble empirical mode decomposition and support vector regression[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(10): 2196-2204.

References

[1] ZHU Xing, XU Qiang, TANG Minggao, et al. Comparison of two optimized machine learning models for predicting displacement of rainfall-induced landslide: a case study in Sichuan province, China[J]. Engineering Geology, 2017, 218: 213-222.
[2] SEGONI S, PICIULLO L, GARIANO S L. A review of the recent literature on rainfall thresholds for landslide occurrence[J]. Landslides, 2018, 15(8): 1483-1501.
[3] SEGONI S, PICIULLO L, GARIANO S L. Preface: landslide early warning systems: monitoring systems, rainfall thresholds, warning models, performance evaluation and risk perception[J]. Natural Hazards and Earth System Sciences, 2018, 18(12): 3179-3186.
[4] MIAO Shengjun, HAO Xin, GUO Xuelian, et al. Displacement and landslide forecast based on an improved version of Saito's method together with the Verhulst-grey model[J]. Arabian Journal of Geosciences, 2017, 10(3): 53.
[5] YIN Yueping, WANG Hongde, GAO Youlong, et al. Real-time monitoring and early warning of landslides at relocated Wushan town, the Three Gorges Reservoir, China[J]. Landslides, 2010, 7(3): 339-349.
[6] VAN KHOA V, TAKAYAMA S. Wireless sensor network in landslide monitoring system with remote data management[J]. Measurement, 2018, 118: 214-229.
[7] CORSINI A, MULAS M. Use of ROC curves for early warning of landslide displacement rates in response to precipitation (Piagneto landslide, Northern Apennines, Italy)[J]. Landslides, 2017, 14(3): 1241-1252.
[8] LIU Junqi, TANG Huiming, LI Qi, et al. Multi-sensor fusion of data for monitoring of Huangtupo landslide in the Three Gorges Reservoir (China)[J]. Geomatics, Natural Hazards and Risk, 2018, 9(1): 881-891.
[9] MUFUNDIRWA A, FUJII Y, KODAMA J. A new practical method for prediction of geomechanical failure-time[J]. International Journal of Rock Mechanics and Mining Sciences, 2010, 47(7): 1079-1090.
[10] HEGDE J, ROKSETH B. Applications of machine learning methods for engineering risk assessment—a review[J]. Safety Science, 2020, 122: 104492.
[11] 邓聚龙. 灰色数理资源科学导论[M]. 武汉:华中科技大学出版社,2010. DENG Julong. Introduction to grey mathematical resource science[M]. Wuhan: Huazhong University of Science & Technology Press, 2010.
[12] 殷坤龙, 晏同珍. 滑坡预测及相关模型[J]. 岩石力学与工程学报, 1996, 15(1): 1-8. YIN Kunlong, YAN Tongzhen. Landslide prediction and relevant models[J]. Chinese Journal of Rock Mechanics and Engineering, 1996, 15(1): 1-8.
[13] 秦四清. 斜坡失稳过程的非线性演化机制与物理预报[J]. 岩土工程学报, 2005, 27(11): 1241-1248. QIN Siqing. Nonlinear evolutionary mechanisms and physical prediction of instability of planar-slip slope[J]. Chinese Journal of Geotechnical Engineering, 2005, 27(11): 1241-1248.
[14] KUMAR S, RANGAN P V, RAMESH M V. Design and validation of wireless communication architecture for long term monitoring of landslides[M]//MIKOŠ M, ARBANAS Ž, YIN Y P, et al. Advancing Culture of Living with Landslides. Cham: Springer, 2017: 51-60.
[15] DU Juan, YIN Kunlong, LACASSE S. Displacement prediction in colluvial landslides, Three Gorges Reservoir, China[J]. Landslides, 2012, 10(2): 203-218.
[16] YAO Wei, ZENG Zhigang, LIAN Cheng, et al. Training enhanced reservoir computing predictor for landslide displacement[J]. Engineering Geology, 2015, 188: 101-109.
[17] CAI Zhenglong, XU Weiya, MENG Yongdong, et al. Prediction of landslide displacement based on GA-LSSVM with multiple factors[J]. Bulletin of Engineering Geology and the Environment, 2016, 75(2): 637-646.
[18] LIAN Cheng, ZENG Zhigang, YAO Wei, et al. Multiple neural networks switched prediction for landslide displacement[J]. Engineering Geology, 2015, 186: 91-99.
[19] LIAN Cheng, ZENG Zhigang, YAO Wei, et al. Ensemble of extreme learning machine for landslide displacement prediction based on time series analysis[J]. Neural Computing and Applications, 2014, 24(1): 99-107.
[20] HUANG Faming, YIN Kunlong, ZHANG Guirong, et al. Landslide displacement prediction using discrete wavelet transform and extreme learning machine based on chaos theory[J]. Environmental Earth Sciences, 2016, 75(20): 1376.
[21] ELMAN J L. Finding structure in time[J]. Cognitive Science, 1990, 14(2): 179-211.
[22] MA Junwei, TANG Huiming, LIU Xiao, et al. Probabilistic forecasting of landslide displacement accounting for epistemic uncertainty: a case study in the Three Gorges Reservoir area, China[J]. Landslides, 2018, 15(6): 1145-1153.
[23] CARLÀ T, INTRIERI E, DI TRAGLIA F, et al. A statistical-based approach for determining the intensity of unrest phases at Stromboli volcano (Southern Italy) using one-step-ahead forecasts of displacement time series[J]. Natural Hazards, 2016, 84(1): 669-683.
[24] XU Shiluo, NIU Ruiqing. Displacement prediction of Baijiabao landslide based on empirical mode decomposition and long short-term memory neural network in Three Gorges area, China[J]. Computers & Geosciences, 2018, 111: 87-96.
[25] LIAN Cheng, ZENG Zhigang, YAO Wei, et al. Displacement prediction model of landslide based on a modified ensemble empirical mode decomposition and extreme learning machine[J]. Natural Hazards, 2013, 66(2): 759-771.
[26] YANG Beibei, YIN Kunlong, LACASSE S, et al. Time series analysis and long short-term memory neural network to predict landslide displacement[J]. Landslides, 2019, 16(4): 677-694.
[27] DU Han, SONG Danqing, CHEN Zhuo, 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.
[28] HUANG N E, SHEN Zheng, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings og the Royal Society A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995.
[29] WU Zhaohua, HUANG N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
[30] 柳治谱, 王勇. 基于无线传感器网络的坡面异常信号检测方法研究[J]. 传感技术学报, 2017, 30(10): 1536-1541. LIU Zhipu, WANG Yong. Research on method of slope abnormal signal detection based on wireless sensor network[J]. Chinese Journal of Sensors and Actuators, 2017, 30(10): 1536-1541.
[31] 杨帆, 许强, 范宣梅, 等. 基于时间序列与人工蜂群支持向量机的滑坡位移预测研究[J]. 工程地质学报, 2019, 27(4): 880-889. YANG Fan, XU Qiang, FAN Xuanmei, et al. Prediction of landslide displacement time series based on support vector regression machine with artificial bee colony algorithm[J]. Journal of Engineering Geology, 2019, 27(4): 880-889.
[32] 邓冬梅, 梁烨, 王亮清, 等. 基于集合经验模态分解与支持向量机回归的位移预测方法: 以三峡库区滑坡为例[J]. 岩土力学, 2017, 38(12): 3660-3669. DENG Dongmei, LIANG Ye, WANG Liangqing, et al. Displacement prediction method based on ensemble empirical mode decomposition and support vector machine regression: a case of landslides in Three Gorges Reservoir area[J]. Rock and Soil Mechanics, 2017, 38(12): 3660-3669.
[33] ZHANG Xun, LAI K K, WANG Shouyang. A new approach for crude oil price analysis based on empirical mode decomposition[J]. Energy Economics, 2008, 30(3): 905-918.
[34] GUO Zizheng, CHEN Lixia, GUI Lei, et al. Landslide displacement prediction based on variational mode decomposition and WA-GWO-BP model[J]. Landslides, 2020, 17(3): 567-583.
[35] 刘人杰, 黄健, 剪鑫磊, 等. EEMD-GA-SVM模型在滑坡位移预测中的应用[J]. 人民长江, 2019, 50(11): 134-139. LIU Renjie, HUANG Jian, JIAN Xinlei, et al. Landslide deformation prediction based on EEMD-GA-SVM model[J]. Yangtze River, 2019, 50(11): 134-139.
[36] LI Huajin, XU Qiang, HE Yusen, et al. Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models[J]. Landslides, 2018, 15(10): 2047-2059.
[37] LING Qing, ZHANG Qin, ZHANG Jing, et al. Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China[J]. Natural Hazards, 2021, 108(1): 925-946.
[38] ZHANG Yonggang, YANG Lining. A novel dynamic predictive method of water inrush from coal floor based on gated recurrent unit model[J]. Natural Hazards, 2021, 105(2): 2027-2043.