A Displacement Prediction Model for Dahua Landslide

WANG Wei; ZOU Li-fang; ZHOU Qian-yao; JIANG Yu-hang; CHEN Hong-jie; XU Wei-ya

doi:10.11988/ckyyb.20210462

PDF(7289 KB)

Journal of Changjiang River Scientific Research Institute ›› 2022, Vol. 39 ›› Issue (9) : 56-64. DOI: 10.11988/ckyyb.20210462

ENGINEERING SAFETY AND DISASTER PREVENTION

A Displacement Prediction Model for Dahua Landslide

WANG Wei^1,2, ZOU Li-fang³, ZHOU Qian-yao^1,2, JIANG Yu-hang^1,2, CHEN Hong-jie⁴, XU Wei-ya^1,2

Author information +

History +

Abstract

A method of landslide displacement prediction based on least squares support vector machine (LSSVM) is proposed to overcome the shortcomings of traditional prediction for Dahua landslide in Lancang River basin as a case study. The original sequence is decomposed and reconstructed into trend item and fluctuation item based on time series analysis and ensembled empirical mode decomposition (EEMD). The displacement of trend item is affected by internal factors of landslide,and the least square method and polynomial equation are used for fitting prediction. The displacement of fluctuation item is affected by periodic factors such as reservoir water level,rainfall and groundwater level. The grey correlation degree method and kernel principal component analysis (KPCA) are combined to screen and reduce the dimension of input factors,and then PSO-LSSVM coupling model is used for predicting the fluctuation item. Finally,the cumulative predicted displacement is obtained by adding the predicted displacement of trend item and fluctuation item,and the model prediction accuracy is quantitatively analyzed. Results demonstrate that the EEMD-KPCA-PSO-LSSVM combined model has a sound prediction performance with higher prediction accuracy than traditional BP neural network,LSSVM and other single models.

Key words

water-induced landslide / displacement prediction / EEMD / KPCA / LSSVM

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

WANG Wei, ZOU Li-fang, ZHOU Qian-yao, JIANG Yu-hang, CHEN Hong-jie, XU Wei-ya. A Displacement Prediction Model for Dahua Landslide[J]. Journal of Changjiang River Scientific Research Institute. 2022, 39(9): 56-64 https://doi.org/10.11988/ckyyb.20210462

References

[1] 周家文.水动力型滑坡灾害机理与防控[J].工程科学与技术:专栏简介,2019,51(4):11-11.
[2] 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.
[3] 韩斐,牛瑞卿,李士垚,等.基于变分模态分解和深度置信神经网络模型的滑坡位移预测[J].长江科学院院报,2020,37(8):61-68.
[4] 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.
[5] 杨背背,殷坤龙,杜娟.基于时间序列与长短时记忆网络的滑坡位移动态预测模型[J].岩石力学与工程学报,2018,37(10):2334-2343.
[6] 鄢好,李绍红,吴礼舟.联合多种数据驱动建模方法的滑坡位移预测研究[J].工程地质学报,2019,27(2):459-465.
[7] 文静.基于小波与灰色模型的滑坡时间预测预报[D].西安:长安大学,2013.
[8] ZHOU Z,ZHANG J,PENG J.The Application of Wavelet Analysis and Support Vector Machine Coupling Model in Displacement Prediction of Landslide[J].Electronic Journal of Geotechnical Engineering,2015,20(16):6823-6833.
[9] 姚琦,牛瑞卿,赵金童,等.基于经验模态分解-支持向量机的滑坡位移预测方法研究[J].安全与环境工程,2017,24(1):26-32.
[10] 孟繁林.集合经验模态分解的理论及应用研究[D].镇江:江苏科技大学,2013.
[11] 许强,汤明高,徐开祥,等.滑坡时空演化规律及预警预报研究[J].岩石力学与工程学报,2008,27(6):1104-1112.
[12] 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):148-161.
[13] CAI Z,XU W,MENG Y,et al.Prediction of Landslide Displacement Based on GA-LSSVM with Multiple Factors[J].Bulletin of Engineering Geology & the Environment,2016,75(2):637-646.
[14] PRADHAN B,LEE S.Landslide Risk Analysis using Artificial Neural Network Model Focusing on Different Training Sites[J].International Journal of Physical Sciences,2009,4(2):1-15.
[15] 胡军,董建华,王凯凯,等.边坡稳定性的CPSO-BP模型研究[J].岩土力学,2016,37(增刊1):577-582.
[16] SUYKENS J,VANDEWALLE J.Least Squares Support Vector Machines Classiﬁers[J].Neural Process Letters,1999,9(3):293-300.
[17] 刘清山,汤俊.基于相空间重构和最小二乘支持向量机的滑坡预测[J].浙江水利水电专科学校学报,2010,22(2):58-60.
[18] 韩贺鸣,张磊,施斌,等.基于光纤监测和PSO-SVM模型的马家沟滑坡深部位移预测研究[J].工程地质学报,2019,27(4):853-861.
[19] 王念秦,朱文博,郭有金.基于PSO-SVM模型的滑坡易发性评价[J].长江科学院院报,2021,38(4):56-62.
[20] WU Z,HUANG N E.Ensemble Empirical Mode Decomposition:A Noise-assisted Data Analysis Method[J].Advances in Adaptive Data Analysis,2009(1):1-41.
[21] 张浩然,韩正之,李昌刚.支持向量机[J].计算机科学,2002,29(12):135-137.
[22] 廉城.基于极限学习的重大工程灾变滑坡预测研究[D].武汉:华中科技大学,2014.
[23] 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.