文章摘要
张 柯, 杨 杰, 程 琳.基于ABC-SVM的土石坝变形监测模型Journal of Water Resources and Water Engineering[J].,2017,28(4):199-204
基于ABC-SVM的土石坝变形监测模型
Earth-rockfill dam deformation monitoring model based on ABC-SVM
  
DOI:10.11705/j.issn.1672-643X.2017.04.34
中文关键词: 土石坝变形  监测模型  人工蜂群算法  支持向量机  参数寻优
英文关键词: deformation of earth-rockfill dam  monitoring model  artificial bee colony algorithm  support vector machines  parameter optimization
基金项目:国家自然科学基金项目(51409205);西安理工大学水利水电学院青年科技创新团队项目(2016ZZKT-14)
Author NameAffiliation
ZHANG Ke1, YANG Jie1,2, CHENG Lin1,2 1.西安理工大学 水利水电学院 陕西 西安 710048 2.陕西省西北旱区生态水利工程重点实验室 陕西 西安 710048 
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中文摘要:
      针对土石坝变形具有较强的非线性特征,传统统计模型预测精度不高,误差较大的问题。引入支持向量机模型(SVM),并采用人工蜂群算法(ABC)对支持向量机的关键参数惩罚因子C和核函数参数σ进行寻优,提高模型的拟合和预测精度,建立ABC-SVM模型应用于土石坝变形监测。实例验证分析表明:与传统多元回归模型和SVM模型相比,ABC-SVM模型预测精度高、泛化能力强。利用ABC-SVM模型对土石坝变形进行预测效果良好,可在大坝安全监测领域推广应用。
英文摘要:
      As the Earth-rockfill dam deformation has strong nonlinear characteristics, and the traditional statistical model is often not accurate and the error is big, the support vector machine (SVM) model is introduced. The key parameters penalty factor C and kernel function parameter σ of support vector machine are optimized by artificial bee colony algorithm (ABC) to improve the fitting and prediction accuracy, and the ABC-SVM model is established to monitor the deformation of earth-rockfill dam. The experiment shows that the ABC-SVM model has high prediction accuracy and strong generalization ability compared with the traditional multiple regression model and SVM model. It has a good effect to use ABC-SVM model to predict the deformation of earth-rock dam, and the model can be used in the field of dam safety monitoring.
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