@Article{cmc.2020.012537,
AUTHOR = {Mengcheng Sun, Weiya Xu, Huanling Wang, Qingxiang Meng, Long Yan, Wei-Chau Xie},
TITLE = {A Novel Hybrid Intelligent Prediction Model for Valley Deformation: A Case Study in Xiluodu Reservoir Region, China},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {66},
YEAR = {2021},
NUMBER = {1},
PAGES = {1057--1074},
URL = {http://www.techscience.com/cmc/v66n1/40497},
ISSN = {1546-2226},
ABSTRACT = {The narrowing deformation of reservoir valley during the initial operation period threatens the long-term safety of the dam, and an accurate prediction
of valley deformation (VD) remains a challenging part of risk mitigation. In order
to enhance the accuracy of VD prediction, a novel hybrid model combining
Ensemble empirical mode decomposition based interval threshold denoising
(EEMD-ITD), Differential evolutions—Shuffled frog leaping algorithm
(DE-SFLA) and Least squares support vector machine (LSSVM) is proposed.
The non-stationary VD series is firstly decomposed into several stationary subseries by EEMD; then, ITD is applied for redundant information denoising on
special sub-series, and the denoised deformation is divided into the trend and periodic deformation components. Meanwhile, several relevant triggering factors
affecting the VD are considered, from which the input features are extracted by
Grey relational analysis (GRA). After that, DE-SFLA-LSSVM is separately performed to predict the trend and periodic deformation with the optimal inputs. Ultimately, the two individual forecast components are reconstructed to obtain the final
predicted values. Two VD series monitored in Xiluodu reservoir region are utilized
to verify the proposed model. The results demonstrate that: (1) Compared with Discrete wavelet transform (DWT), better denoising performance can be achieved by
EEMD-ITD; (2) Using GRA to screen the optimal input features can effectively
quantify the deformation response relationship to the triggering factors, and reduce
the model complexity; (3) The proposed hybrid model in this study displays superior performance on some compared models (e.g., LSSVM, Backward Propagation
neural network (BPNN), and DE-SFLA-BPNN) in terms of forecast accuracy.},
DOI = {10.32604/cmc.2020.012537}
}