Combined forecasting model with CEEMD-LCSS reconstruction and the ABC-SVR method for landslide displacement prediction. (15th April 2021)

• Record Type: Journal Article
• Authors: Zhang, Junrong; Tang, Huiming; Tannant, Dwayne D.; Lin, Chengyuan; Xia, Ding; Liu, Xiao; Zhang, Yongquan; Ma, Junwei
• Is Part Of: Journal of cleaner production. Volume 293(2021)
• Publication Date: 2021-04-15
• Subjects: Combined forecasting model -- Complete ensemble empirical mode decomposition (CEEMD) -- Longest common subsequence (LCSS) -- Inducing factors -- Artificial bee colony algorithm (ABC) -- Support vector regression (SVR); Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2021.126205
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: In the Three Gorges Reservoir area (TGRA), landslides typically exhibit slow-moving characteristic deformations in response to seasonal changes in precipitation and reservoir level. This study proposed an optimal combination of methods in which the frequency components of decomposed inducing factors are considered for predicting landslide displacement. In preprocessing, the monitored surface displacements are decomposed into trend displacement and periodic displacement components via ensemble empirical mode decomposition (EEMD); the time series of the inducing factors (rainfall and reservoir level) were reconstructed and decomposed into high- and low-frequency time series via complete ensemble empirical mode decomposition (CEEMD) and t -test methods, among which two dominant inducing factors are identified by analyzing the longest common subsequence (LCSS) distance of the normalized data. In the prediction of the periodic displacement component, three optimal methods that use the support vector regression (SVR) model have been considered for comparison: the artificial bee colony (ABC) algorithm, the genetic algorithm (GA) and particle swarm optimization (PSO). The results demonstrate that the combination of CEEMD-LCSS reconstruction and ABC-SVR yields a robust model with remarkable prediction performance, which can increase the prediction accuracy of landslide displacements by 54% at most in the evaluation of RMSE.