基于PSO-RVM模型的大坝安全监控研究  

Study on dam safety monitoring based on PSO-RVM model

作　　者：宋培玉 杨浩东 王嘉华 SONG Peiyu;YANG Haodong;WANG Jiahua(Nanjing Design and Research Institute Company Limited,China Coal Technology and Engineering Group,Nanjing 210019,China;Jiangsu South Water Technology Co.,LTD,Nanjing 210012,China;College of Science,Hohai University,Nanjing 210098,China)

机构地区：[1]中煤科工集团南京设计研究院有限公司,江苏南京210019 [2]江苏南水科技有限公司,江苏南京210012 [3]河海大学理学院,江苏南京210098

出　　处：《水利信息化》2020年第4期31-36,共6页Water Resources Informatization

摘　　要：为提高大坝安全监控模型的预报精度和时间,利用粒子群算法(PSO)对相关向量机的关键核参数进行寻优,通过建立大坝安全监控模型与实际值比较,从而对RVM模型进行稀疏、学习、泛化等性能的分析研究。采用某实际工程实测视准线位移监测对模型进行验证,并通过均方根、标准均方和平均绝对百分比等误差,对模型预测的准确性、稳定性和可信程度进行评价。研究表明,PSO-RVM模型的泛化性能明显优于传统的RVM模型,应用于大坝安全监测建模是可行的。In order to improve the prediction accuracy and time of the dam safety monitoring model,particle swarm optimization(PSO)is used to optimize the key kernel parameters of the relevant vector machine.By comparing the dam safety monitoring model with the actual value,the sparse performance,learning performance and generalization performance of the RVM model are analyzed and studied.The model is verified by the observation of visual alignment displacement in an actual project.The accuracy,stability and reliability of the model prediction are evaluated by means of errors such as root mean square,standard mean square and average absolute percentage.The research shows that the generalization performance of PSO-RVM is obviously better than that of traditional RVM.And it is feasible to apply PSO-RVM to dam safety monitoring modeling.

关 键 词：安全监控模型 PSO-SVM模型 大坝安全监控 粒子群算法 模型参数 模型分析 

分 类 号：TV698.1[水利工程—水利水电工程]