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SCI论文:Comparison of two optimized machine learning models for predicting displacement of rainfall-induced landslide

标题: Comparison of two optimized machine learning models for predicting displacement of rainfall-induced landslide: A case study in Sichuan Province, China

作者: : Zhu, X (Zhu, Xing); Xu, Q (Xu, Qiang)* ; Tang, MG (Tang, Minggao) ; Nie, W (Nie, Wen); Ma, SQ (Ma, Shuqi); Xu, ZP (Xu, Zhipeng)

期刊: ENGINEERING GEOLOGY

卷: 218 页: 213-222

DOI: 10.1016/j.enggeo.2017.01.022

出版年: FEB 23 2017

摘要:

Evaluation and prediction of displacement by specific models help in forecasting geo-hazards. Among the various available predictive tools, Least Square Support Vector Machines (LSSVM) model optimized with Genetic Algorithm, namely GA-LSSVM, is commonly used to empirically forecast landslide displacement due to its capability of processing non-linear complex systems. Another improved hybrid model composed of Double Exponential Smoothing (DES) and LSSVM considers measured displacement and precipitation time series to estimate the one-step ahead displacement evolution of rain-induced landslide. Here, the modelling process and accuracy of these two models are presented, and their predictive performances are evaluated by the root mean squared error (RMSE), mean absolute percentage error (MAPE), accuracy factor (AF), and correlation coefficient (R). A slowly-moving landslide on gently dipping rocky slope located in Sichuan Province of China was chosen as the case study for its deformation triggered by intense seasonal rainfall. The application results indicated that both GA-LSSVM and DES-LSSVM models were suitable for accurately predicting the landslide displacement on the basis of precipitation and displacement observations. Furthermore, comparison results show that DES-LSSVM model can provide the better predictive accuracy, with RMSE and MAPE values of 0.059 mm and 0.004%, respectively.

关键词: Genetic Algorithm; Least Squares Support Vector Machines; Double Exponential Smoothing; Landslide; High-accuracy prediction.

入藏号: WOS:000394476500018

链接: http://www.sciencedirect.com/science/article/pii/S0013795217301060

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