A Landslide Displacement Prediction Method of Particle Swarm Optimization Combined with Support Vector Machine Regression Based on Recursive Feature Elimination Selection

TANG Feifei; HU Jiaying; MA Ying; ZHOU Zhelin; WANG Jun; HAO Yafei

doi:10.3724/j.gyjzG23071806

Volume 54 Issue 11

Nov. 2024

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TANG Feifei, HU Jiaying, MA Ying, ZHOU Zhelin, WANG Jun, HAO Yafei. A Landslide Displacement Prediction Method of Particle Swarm Optimization Combined with Support Vector Machine Regression Based on Recursive Feature Elimination Selection[J]. INDUSTRIAL CONSTRUCTION, 2024, 54(11): 50-60. doi: 10.3724/j.gyjzG23071806

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A Landslide Displacement Prediction Method of Particle Swarm Optimization Combined with Support Vector Machine Regression Based on Recursive Feature Elimination Selection

doi: 10.3724/j.gyjzG23071806

1. College of Smart City, Chongqing Jiaotong University, Chognqing 402260, China;
2. China 19th Metallurgical Group Corporation Limited, Chengdu 610031, China;
3. CICT Connected and Intelligent Technologies Co., Chognqing 400030, China;
4. Explosives Corporation Limited, CGGC, Chognqing 401121, China

Received Date: 2023-07-18
Available Online: 2024-12-05

Abstract

Abstract

Affected by the seasonal precipitation and changes in water levels of reservoirs, parts of mountains will generate landslides in which the cumulative displacement-time curve of some landslides exhibits obvious "step type" dynamic deformation characteristics. In the light of that displacement, a landslide displacement prediction model was proposed, which was combined the particle swarm optimization (PSO) algorithm with the support vector regression (SVR) based on the recursive feature elimination (RFE) algorithm. and the research was performed combined with the Xinpu landslide. Firstly, the methods of outlier removal and missing value filling for landslide displacement data were explored. Outlier rejection was adopted a median-based method with ensemble empirical mode decomposition, and missing value filling was adopted a statistical variable-based method. Then, the "step type" cumulative displacement of landslides was split into the trend and period terms by the exponential smoothing method. The displacement of the trend term was fitted by Fourier curve. For the displacement of the periodic term, the influence factors with high correlation with the displacement of the periodic term were selected based on the SVR-RFE method, and the prediction model for the displacement of the periodic term was established. Subsequently, the prediction model parameters were optimized by the PSO algorithm. Finally, the predicted displacement of the period term was superimposed on the predicted displacement of the trend term to obtain the final predicted cumulative displacement of landslides, in which the goodness of fit was 0.999, the root mean square error was 9.974 mm, and the average absolute error was 7.037 mm. Comparing the proposed model with the GSCV-SVR model and the GA-SVR model, the proposed model was of a strong predictive capability for sudden displacement and was suitable for risk warning during periods of accelerated displacement changes of "step type" displacement of landslides.
- landslide,
- displacement prediction,
- PFE,
- SVR,
- PSO

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References(24)

References

[1]	许强, 黄润秋, 李秀珍.滑坡时间预测预报研究进展[J].地球科学进展, 2004(3):478-483.
[2]	KALANTAR B, UEDA N, AL-NAJJAR H A H, et al. A comparison between three conditioning factors dataset for landslide prediction in the Sajadrood catchment of Iran[C]//Proceedings of XXIV ISPRS Congress: ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences.2020:625-635.
[3]	SHIHABUDHEEN K V, 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.
[4]	彭令, 牛瑞卿, 赵艳南, 等. 基于核主成分分析和粒子群优化支持向量机的滑坡位移预测[J]. 武汉大学学报(信息科学版), 2013, 38(2):148-152, 161.
[5]	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): 2674-2680.
[6]	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.
[7]	ZHANG W, XIAO R, SHI B, et al. Forecasting slope deformation field using correlated grey model updated with time correction factor and background value optimization[J/OL]. Engineering Geology, 2019, 260[2023-07-18]. https://doi.org/10.1016/j.enggeo.2019.105215.
[8]	宋丽伟.基于经验模态分解和LSTM模型的滑坡位移预测[J].人民长江, 2020, 51(5):144-148.
[9]	王朝阳, 李丽敏, 温宗周, 等.基于时间序列和CNN-LSTM的滑坡位移动态预测[J].国外电子测量技术, 2022, 41(3):1-8.
[10]	LI Y Y, SUN R L, YIN K L, et al. Forecasting of landslide displacements using a chaos theory based wavelet analysis-Volterra filter model[R/OL]. 2019[2023-07-18]. chrome-extension://bnjoienjhhclcabnkbhhfndecoipmcdg/background/jgpdf/layout/index.html?file=https://www.nature.com/articles/s41598-019-56405-y.pdf.
[11]	陈曦, 高雅萍, 涂锐.基于EMD-TAR组合模型的滑坡位移预测研究[J].人民珠江, 2022, 43(3):96-101 , 108.
[12]	GUO Z Z, CHEN L X, GUI L, et al. Landslide displacement prediction based on variational mode decomposition and WA-GWO-BP model[J]. Landslides, 2020, 17(3):567-583.
[13]	DENG D M, LIANG Y, WANG L Q, 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]. Yantu Lixue/Rock and Soil Mechanics, 2017, 38(12):3660-3669.
[14]	李秀珍, 许强.滑坡预报模型和预报判据[J].灾害学, 2003, 18(4):71-78.
[15]	李强, 李端有.滑坡位移监测动态预报时间序列分析技术研究[J].长江科学院院报, 2005, 22(6):22-25.
[16]	XING Y, YUE J P, CHENG C. Interval estimation of landslide displacement prediction based on time series decomposition and long short-term memory network[J].IEEE Access, 2020, 8:3187-3196.
[17]	SHARIFI S, HENDRY M, MACCIOTTA R, et al. Evaluation of filtering methods for use on high-frequency measurements of landslide displacements[J]. Natural Hazards and Earth System Sciences, 2022, 22(2):411-430.
[18]	罗袆沅, 蒋亚楠, 许强, 等.GPS滑坡位移监测时序分析与组合建模预测[J].防灾科技学院学报, 2020, 22(4):20-28.
[19]	LIN H, LU Y, DING R, et al. A multi-feature fusion apple classification method based on image processing and improved SVM[J]. Agricultural Biotechnology, 2022, 11(5):84-91.
[20]	VAPNIK V N. The nature of statistical learning theory[M]. Berlin: Springer, 1995.
[21]	GUYON I, WESTON J, BARNHILL S, et al. Gene Selection for cancer classification using support vector machines[J]. Machine Learning, 2002, 46(1/2/3):389-422.
[22]	YIN Y H, JANG-JACCARD J, XU W, et al. IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset[J/OL]. Journal of Big Data, 2023, 10 [2023-07-18]. https://doi.org/10.1186/s40537-023-00694-8.
[23]	CROZIER M J, EYLES R J. Assessing the probability of rapid mass movement[C]//Proceedings of the 3rd Australia-New Zealand Conference on Geomechanics. Wellington: New Zealand Institution of Engineers, 1980:47-51.
[24]	QI L P. A course recommender system of MOOC based on collaborative filtering algorithm with improved pearson correlation coefficient[D]. 武汉:华中师范大学, 2019.

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