Prediction of landslide displacement based on EEMD-Prophet-LSTM

doi:10.7523/j.ucas.2022.002

Abstract

Abstract: For the unsteady process of step-type landslide displacement, a method combining ensemble empirical mode decomposition (EEMD), Prophet, and long short time memory network (LSTM) to predict landslide displacement is proposed. The displacement data of Baishuihe landslide was taken as examples. The displacement time series was decomposed into residual(RES) and several intrinsic mode functions(IMF) by EEMD. The superimposition of IMFS which included periodic factors and random factors was considered as a volatility item, and the RES was regarded as a trend term. The trending term was fitted by the Prophet and the the volatility term was predicted by LSTM. The addition of the two prediction results was the predictied value of the landslide displacement. The results show that the coefficient of determination(R²) of the EEMD-Prophet-LSTM model is above 0.98 for Baishuihe landslide displacement prediction, which is better than traditional machine learning methods such as support vector machine and artificial neural network. Moreover, the prediction accuracy R² of this method for each monitoring point of the Bazimen landslide is also above 0.96, which proves the applicability of this method.

Key words: landslide displacement, time series, ensemble empirical mode decomposition, Prophet, long short time memory network

CLC Number:

P642.22

WANG Zhenhao, NIE Wen, XU Hanhua, JIAN Wenbin. Prediction of landslide displacement based on EEMD-Prophet-LSTM[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(4): 514-522.

References

[1] 孙国庆, 陈方, 于博, 等. 2001-2017年尼泊尔中部地区滑坡变化及其影响因素[J]. 中国科学院大学学报, 2020, 37(3):308-316. DOI:10.7523/j.issn.2095-6134.2020.03.003.
[2] 王卫东, 瞿霞, 刘攀, 等. 基于最优权重联合模型的滑坡位移预测研究[J]. 灾害学, 2018, 33(1):59-64. DOI:10.3969/j.issn.1000-811X.2018.01.012.
[3] 刘星, 吕孝雷. 基于卡尔曼滤波的PS-InSAR地表形变预测方法[J]. 中国科学院大学学报, 2017, 34(6):743-750. DOI:10.7523/j.issn.2095-6134.2017.06.011.
[4] 李仕波, 李德营, 张玉恩, 等. 基于LS-SVM模型的白水河滑坡台阶状位移预测[J]. 长江科学院院报, 2019, 36(4):55-59, 76. DOI:10.11988/ckyyb.20170944.
[5] 徐峰, 汪洋, 杜娟, 等. 基于时间序列分析的滑坡位移预测模型研究[J]. 岩石力学与工程学报, 2011, 30(4):746-751.
[6] 赵亚伟, 陈艳晶. 多维时间序列的组合预测模型[J]. 中国科学院大学学报, 2016, 33(6):825-833. DOI:10.7523/j.issn.2095-6134.2016.06.015.
[7] Rukhaiyar S, Alam M N, Samadhiya N K. A PSO-ANN hybrid model for predicting factor of safety of slope[J]. International Journal of Geotechnical Engineering, 2018, 12(6):556-566. DOI:10.1080/19386362.2017.1305652.
[8] 彭令, 牛瑞卿, 吴婷. 时间序列分析与支持向量机的滑坡位移预测[J]. 浙江大学学报(工学版), 2013, 47(9):1672-1679.
[9] 张俊, 殷坤龙, 王佳佳, 等. 基于时间序列与PSO-SVR耦合模型的白水河滑坡位移预测研究[J]. 岩石力学与工程学报, 2015, 34(2):382-391. DOI:10.13722/j.cnki.jrme.2015.02.017.
[10] Zhou C, Yin K L, Cao Y, et al. Application of time series analysis and PSO-SVM model in predicting the Bazimen landslide in the Three Gorges Reservoir, China[J]. Engineering Geology, 2016, 204:108-120. DOI:10.1016/j.enggeo.2016.02.009.
[11] 雷雨, 蔡宏兵, 赵丹宁. 利用灰色关联极限学习机预报日长变化[J]. 中国科学院大学学报, 2015, 32(5):588-593, 604. DOI:10.7523/j.issn.2095-6134.2015.05.003.
[12] Kv S, Pillai G N, Peethambaran B. Prediction of landslide displacement with controlling factors using extreme learning adaptive neuro-fuzzy inference system (ELANFIS)[J]. Applied Soft Computing, 2017, 61:892-904. DOI:10.1016/j.asoc.2017.09.001.
[13] Huang F M, Yin K L, Zhang G R, et al. Landslide displacement prediction using discrete wavelet transform and extreme learning machine based on chaos theory[J]. Environmental Earth Sciences, 2016, 75(20):1-18. DOI:10.1007/s12665-016-6133-0.
[14] 李麟玮, 吴益平, 苗发盛. 基于灰狼支持向量机的非等时距滑坡位移预测[J]. 浙江大学学报(工学版), 2018, 52(10):1998-2006. DOI:10.3785/j.issn.1008-973X.2018.10.020.
[15] 李麟玮, 吴益平, 苗发盛, 等. 基于变分模态分解与GWO-MIC-SVR模型的滑坡位移预测研究[J]. 岩石力学与工程学报, 2018, 37(6):1395-1406. DOI:10.13722/j.cnki.jrme.2017.1508.
[16] 张凯, 张科, 保瑞, 等. 基于优化经验模态分解和聚类分析的滑坡位移智能预测研究[J]. 岩土力学, 2021, 42(1):211-223. DOI:10.16285/j.rsm.2020.1300.
[17] Wu Z H, Huang N E. Ensemble empirical mode decomposition:a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1):1-41. DOI:10.1142/s1793536909000047.
[18] Lian C, Zeng Z G, Yao W, 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. DOI:10.1007/s11069-012-0517-6.
[19] 顾鹏程, 王世新, 周艺, 等. 基于时间序列DMSP/OLS夜间灯光数据的GDP预测模型[J]. 中国科学院大学学报, 2019, 36(2):188-195. DOI:10.7523/j.issn.2095-6134.2019.02.006.
[20] 刘艺梁, 殷坤龙, 汪洋, 等. 基于经验模态分解和神经网络的滑坡变形预测研究[J]. 安全与环境工程, 2013, 20(4):14-17. DOI:10.3969/j.issn.1671-1556.2013.04.004.
[21] 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. DOI:10.16285/j.rsm.2017.12.034.
[22] 葛娜, 孙连英, 石晓达, 等. Prophet-LSTM组合模型的销售量预测研究[J]. 计算机科学, 2019, 46(S1):446-451.
[23] Wang Z, Hong T Z, Li H, et al. Predicting city-scale daily electricity consumption using data-driven models[J]. Advances in Applied Energy, 2021, 2:100025. DOI:10.1016/j.adapen.2021.100025.
[24] 田睿, 董绪荣. 小波分解与Prophet框架融合的电离层VTEC预报模型[J]. 系统工程与电子技术, 2021, 43(3):610-622. DOI:10.12305/j.issn.1001-506X.2021.03.03.
[25] 肖亚楠, 周伟, 崔杰, 等. 一种LSTM神经网络和卡尔曼滤波相结合的复合材料承载预测方法[J]. 中国科学院大学学报, 2021, 38(3):374-381. DOI:10.7523/j.issn.2095-6134.2021.03.011.
[26] 包振山, 郭俊南, 谢源, 等. 基于LSTM-GA的股票价格涨跌预测模型[J]. 计算机科学, 2020, 47(S1):467-473.
[27] Bouktif S, Fiaz A, Ouni A L, et al. Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm:comparison with machine learning approaches[J]. Energies, 2018, 11(7):1636. DOI:10.3390/en11071636.
[28] Liu Z Q, Guo D, Lacasse S, et al. Algorithms for intelligent prediction of landslide displacements[J]. Journal of Zhejiang University-SCIENCE A, 2020, 21(6):412-429. DOI:10.1631/jzus.a2000005.
[29] 赵英, 翟源伟, 陈骏君, 等. 基于LSTM-Prophet非线性组合的时间序列预测模型[J]. 计算机与现代化, 2020(9):6-11, 18. DOI:10.3969/j.issn.1006-2475.2020.09.002.