Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

被引:61
作者
Wen, Tao [1 ]
Tang, Huiming [1 ,2 ]
Wang, Yankun [1 ,2 ]
Lin, Chengyuan [1 ]
Xiong, Chengren [2 ]
机构
[1] China Univ Geosci, Fac Engn, Wuhan 430074, Hubei, Peoples R China
[2] China Univ Geosci, Minist Educ, Three Gorges Res Ctr Geohazards, Wuhan 430074, Hubei, Peoples R China
基金
中国国家自然科学基金; 美国国家科学基金会;
关键词
SUPPORT VECTOR MACHINE; PARTICLE SWARM OPTIMIZATION; EXTREME LEARNING-MACHINE; ARTIFICIAL NEURAL-NETWORKS; GENETIC ALGORITHM; PARAMETERS; ENSEMBLE; ACCURACY; KERNEL; PSO;
D O I
10.5194/nhess-17-2181-2017
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048mm and mean absolute percentage error (MAPE) of 1.492% at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601mm and 1.703% at ZG86 and 49.0485 mm, 48.5392mm and 3.131% at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.
引用
收藏
页码:2181 / 2198
页数:18
相关论文
共 62 条
[1]   Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules [J].
Abdi, Mohammad Javad ;
Giveki, Davar .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2013, 26 (01) :603-608
[2]   Reservoir permeability prediction by neural networks combined with hybrid genetic algorithm and particle swarm optimization [J].
Ahmadi, Mohammad Ali ;
Zendehboudi, Sohrab ;
Lohi, Ali ;
Elkamel, Ali ;
Chatzis, Ioannis .
GEOPHYSICAL PROSPECTING, 2013, 61 (03) :582-598
[3]  
Ahmed B, 2013, BANGLADESH LANDSLIDE, V6, P1077
[4]   A corpus-based semantic kernel for text classification by using meaning values of terms [J].
Altinel, Berna ;
Ganiz, Murat Can ;
Diri, Banu .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2015, 43 :54-66
[5]  
[Anonymous], 2011, INT P COMP SCI INF T
[6]  
[Anonymous], 1992, GENETIC PROGRAMMING
[7]  
Brockwell P. J., 2013, TIME SERIES THEORY M, P340
[8]   Prediction of landslide displacement based on GA-LSSVM with multiple factors [J].
Cai, Zhenglong ;
Xu, Weiya ;
Meng, Yongdong ;
Shi, Chong ;
Wang, Rubin .
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT, 2016, 75 (02) :637-646
[9]   Using an extreme learning machine to predict the displacement of step-like landslides in relation to controlling factors [J].
Cao, Ying ;
Yin, Kunlong ;
Alexander, David E. ;
Zhou, Chao .
LANDSLIDES, 2016, 13 (04) :725-736
[10]   Prediction of ground displacements and velocities from groundwater level changes at the Vallcebre landslide (Eastern Pyrenees, Spain) [J].
Corominas, J ;
Moya, J ;
Ledesma, A ;
Lloret, A ;
Gili, JA .
LANDSLIDES, 2005, 2 (02) :83-96