Application of a Novel  Predictive Model Integrating Wavelet Analysis,Boosting Regression Tree and Extreme Learning Machine toLoess Landslide Displacement

LI Hua-jin; XU Qiang; WANG Si-cheng; QI Xing; PENG Da-lei; HE Yu-sen

doi:10.11988/ckyyb.20160529

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Journal of Changjiang River Scientific Research Institute ›› 2017, Vol. 34 ›› Issue (9) : 63-69. DOI: 10.11988/ckyyb.20160529

ENGINEERING SAFETY AND DISASTER PREVENTION

Application of a Novel Predictive Model Integrating Wavelet Analysis,Boosting Regression Tree and Extreme Learning Machine toLoess Landslide Displacement

LI Hua-jin¹,XU Qiang¹,WANG Si-cheng²,QI Xing¹,PENG Da-lei¹,HE Yu-sen³

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Abstract

The deformation evolution process of loess landslide is often nonlinear due to many factors. A theoretical approach named WA-BT-ELM, which is based on wavelet analysis (WA), boosting regression tree (BT) and extreme learning machine (ELM), is proposed to predict loess landslide displacements. By analysis of nonlinear loess landslide time-dependent displacement dataset, the accumulation displacement data signal is decomposed into a series of sub-wavelets. Then, the importance of all the sub-wavelets to the displacement data series is computed by BT algorithm. The highly important sub-wavelets are selected to make further predictions. Furthermore, the predictive results of sub-wavelet and the original landslide displacement series are obtained through ELM algorithms. A case study of Heifangtai landslide in Gansu Province is presented to verify the predictive results. In comparison, the predictive results by using WA-BT-ELM model is faster and more accurate than those by ANN,BPNN,SVM,ELM and WA-ELM model, indicating that the WA-BT-ELM model is effective in loess landslide displacement prediction cases.

Key words

loess landslide / WA-BT-ELM model / displacement prediction / wavelet analysis / boosting regression tree / extreme learning machine

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LI Hua-jin,XU Qiang,WANG Si-cheng,QI Xing,PENG Da-lei,HE Yu-sen. Application of a Novel Predictive Model Integrating Wavelet Analysis,Boosting Regression Tree and Extreme Learning Machine toLoess Landslide Displacement[J]. Journal of Changjiang River Scientific Research Institute. 2017, 34(9): 63-69 https://doi.org/10.11988/ckyyb.20160529

References

[1] 李骅锦,许强,何雨森,等. 甘肃黑方台滑坡滑距参数的BP神经网络模型[J] . 水文地质工程地质,2016, 43(4):141-146,152.
[2] 李骅锦,许强,何雨森,等. WA联合ELM与OS-ELM的滑坡位移预测模型[J] . 工程地质学报,2016, 24(5):721-731.
[3] 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.
[4] QIN S Q, JIAO J J, WANG S J. The Predicable Time Scale of Landslides[J] . Bulletin of Engineering Geological Environment, 2001, 59(4):30-312.
[5] 陈卫兵,李端有. 基于非线性动力学的滑坡位移预测[J] . 长江科学院院报,2006, 23(2):28-30,34.
[6] 杨建国,晏鄂川,程温鸣. 滑坡变形特征的GPS监测信息识别研究[J] . 长江科学院院报,2012, 29(3):24-28.
[7] POUZOLS F M, LENDASSE A, BARROS A B. Autoregressive Time Series Prediction by Means of Fuzzy Inference Systems Using Nonparametric Residual Variance Estimation[J] . Fuzzy Sets System, 2010, 161(4):471-497.
[8] KAYACAN E, ULUTASB B, KAYNAKA O. Grey System Theory-based Models in Time Series Prediction[J] . Expert System Application, 2010, 37(2):1784-1789.
[9] RAN Y F, XIONG G C, LI S S, et al. Study on Deformation Prediction of Landslide Based on Genetic Algorithm and Improved BP Neural Network[J] . Kybernetes: The International Journal of Systems & Cybernetics, 2010, 39(8):1245-1254.
[10] LIU Z, SHAO J, XU W, et al. Comparison on Landslide Nonlinear Displacement Analysis and Prediction with Computation Intelligence Approaches[J] . Landslides, 2013, 11(5):889-896.
[11] 尹光志, 张卫中, 张东明, 等. 基于指数平滑法与回归分析相结合的滑坡预测[J] . 岩土力学, 2007, 28(8): 1725-1728.
[12] LI X Z, KONG J M, WANG Z Y. Landslide Displacement Prediction Based on Combining Method with Optimal Weight[J] . Natural Hazards, 2012, 61(2): 635-646.
[13] HUANG G B, CHEN L, SIEW C K. Universal Approximation Using Incremental Constructive Feed Forward Networks with Random Hidden Nodes[J] . IEEE Transactions on Neural Network, 2006, 17(4): 879-892.
[14] HUANG G B, ZHU Q Y, SIEW C K. Extreme Learning Machine: Theory and Applications[J] . Neurocomputing, 2006, 70(1-3):489-501.
[15] ZHU Q Y, QIN A K, SUGANTHAN P N, et al. Evolutionary Extreme Learning Machine[J] . Pattern Recognition, 2005, 38(10):1759-1763.
[16] HUANG G B. Learning Capability and Storage Capacity of Two-hidden-layer Feed Forward Networks[J] . IEEE Transactions on Neural Network, 2003, 14(2):274-281.
[17] HUANG G B, BABRI H A. Upper Bounds on the Number of Hidden Neurons in Feed Forward Networks with Arbitrary Bounded Nonlinear Activation Functions[J] . IEEE Transactions on Neural Network, 1998, 9(1):224-229.
[18] OUAMMI A, DAGDOUGUI H, SACILE R, et al. Monthly and Seasonal Assessment of Wind Energy Characteristics at Four Monitored Locations in Liguria Region (Italy)[J] . Renewable and Sustainable Energy Reviews, 2010, 14(7):1959-1968.
[19] MICHE Y, AKUSOK A, VEGANZONES D, et al. SOM-ELM-Self-Organized Clustering Using ELM[J] . Neurocomputing, 2015, 165(1):238-254.
[20] ZONG W W, HUANG G B. Face Recognition Based on Extreme Learning Machine[J] . Neurocomputing, 2011, 74(16): 2541-2551.
[21] YU Q, MICHE Y, SEVERIN E, et al. Bankruptcy Prediction Using Extreme Learning Machine and Financial Expertise[J] . Neurocomputing, 2014, 128(27):296-302.
[22] REN Fu, WU Xue-ling, ZHANG Kai-xiang, et al. Application of Wavelet Analysis and a Particle Swarm-optimized Support Vector Machine to Predict the Displacement of the Shuping Landslide in the Three Gorges, China[J] . Environmental Earth Sciences, 2015, 73(8):4791-4804.
[23] KAUNDA R B. A Linear Regression Framework for Predicting Subsurface Geometries and Displacement Rates in Deep-seated, Slow-moving Landslides[J] . Engineering Geology, 2010, 114(1/2):1-9.
[24] HE Y, KUSIAK A, OUYANG T, et al. Data-driven Modeling of Truck Engine Exhaust Valve Failures: A Case Study[J] . Journal of Mechanical Science and Technology, 2017, 31(6): 2747-2757.
[25] YU L, WANG S Y, LAI K K. Forecasting Crude Oil Price with an EMD-based Neural Network Ensemble Learning Paradigm[J] . Energy Economics, 2008, 30(5):2623-2635.
[26] GUO Z H, ZHAO W G, LU H Y, et al. Multi-step Forecasting for Wind Speed Using a Modified EMD Based Artificial Neural Network Model[J] . Renewable Energy, 2012, 37(1):241-249.
[27] SAMAR V J, BOPARDIKAR A, RAO R, et al. Wavelet Analysis of Neuroelectric Waveforms: A Conceptual Tutorial[J] . Brain and Language, 1999, 66(1):7-60.
[28] CHELLALI F, KHELLAF A, BEELOUCHRANI A. Wavelet Spectral Analysis of the Temperature and Wind Speed Data at Adrar,Algeria[J] .Renewable Energy,2010,35(6):1214-1219.
[29] SOYKAN C U, EGUCHI T, KOHIN S, et al. Prediction of Fishing Effort Distributions Using Boosted Regression Trees[J] . Ecological Applications, 2014, 24(1):71-83.
[30] RAO C R, MITRA S K. Generalized Inverse of Matrices and Its Applications[M] . Baltimore and London: John Wiley and Sons, 1971.
[31] SERRE D.Theory and Applications[M] .New York:Springer,2002.
[32] ORTEGA J M. Matrix Theory[M] .New York and London:Plenum Press, 1987.