China Power Construction Group Zhongnan Survey Design & Research Institute Co., Ltd., Changsha, China.
College of Water Conservancy and Hydropower, Hohai University, Nanjing, China.
PLoS One. 2022 Jun 1;17(6):e0267434. doi: 10.1371/journal.pone.0267434. eCollection 2022.
A hybrid model integrating chaos theory, support vector machine (SVM) and the difference evolution grey wolf optimization (DEGWO) algorithm is developed to analyze and predict dam deformation. Firstly, the chaotic characteristics of the dam deformation time series will be identified, mainly using the Lyapunov exponent method, the correlation dimension method and the kolmogorov entropy method. Secondly, the hybrid model is established for dam deformation forecasting. Taking SVM as the core, the deformation time series is reconstructed in phase space to determine the input variables of SVM, and the GWO algorithm is improved to realize the optimization of SVM parameters. Prior to this, the effectiveness of DEGWO algorithm based on the fusion of the difference evolution (DE) and GWO algorithm has been verified by 15 sets of test functions in CEC 2005. Finally, take the actual monitoring displacement of Jinping I super-high arch dam as examples. The engineering application examples show that the PSR-SVM-DEGWO model established performs better in terms of fitting and prediction accuracy compared with existing models.
一种融合混沌理论、支持向量机（SVM）和差分进化灰狼优化（DEGWO）算法的混合模型被开发出来，用于分析和预测大坝变形。首先，利用 Lyapunov 指数法、关联维数法和 Kolmogorov 熵法等方法，对大坝变形时间序列的混沌特性进行识别。其次，建立用于大坝变形预测的混合模型。以 SVM 为核心，对变形时间序列进行相空间重构，确定 SVM 的输入变量，并对 GWO 算法进行改进，实现 SVM 参数的优化。在此之前，通过 CEC 2005 中的 15 组测试函数验证了基于差分进化（DE）和 GWO 算法融合的 DEGWO 算法的有效性。最后，以锦屏一级特高拱坝的实际监测位移为例。工程应用实例表明，与现有模型相比，所建立的 PSR-SVM-DEGWO 模型在拟合和预测精度方面表现更好。
