基于改进IGGⅢ-ELM法的混凝土坝变形监测数据粗差识别方法  

Gross error identification method for deformation monitoring data of concrete dams based on improved IGGⅢ-ELM method

作　　者：王岩博 顾冲时[1,2] 石立 顾昊[1,2] 张建中 陆希[3] 吴艳 朱明远 WANG Yanbo;GU Chongshi;SHI Li;GU Hao;ZHANG Jianzhong;LU Xi;WU Yan;ZHU Mingyuan(The national key Laboratory of Water Disaster Prevention,Hohai University,Nanjing 210098,China;College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,China;Northwest Engineering Corporation Limited,Xi’an 710065,China;Xinjiang Institute of Water Resources and Hydropower Research,Urumqi 830049,China)

机构地区：[1]河海大学水灾害防御全国重点实验室,江苏南京210098 [2]河海大学水利水电学院,江苏南京210098 [3]中国电建集团西北勘测设计研究院有限公司,陕西西安710065 [4]新疆水利水电科学研究院,新疆乌鲁木齐830049

出　　处：《水利水电科技进展》2023年第6期89-95,共7页Advances in Science and Technology of Water Resources

基　　金：国家自然科学基金联合基金项目(U2243223);中央高校基本科研业务费专项资金资助项目(B230201011);江苏省水利科技项目(2022024)。

摘　　要：针对混凝土坝变形监测数据存在非线性强、粗差识别与剔除困难等问题,结合改进IGGⅢ法对异常值的抗差能力强、极限学习机(ELM)对数据序列预测效率高和对非线性问题处理能力强、增量ELM寻找最优网络结构速度快等优势,提出了基于改进IGGⅢ-ELM法的混凝土坝变形监测数据粗差识别方法,并采用包含两个调和系数的四段权函数对IGGⅢ-ELM法进行改进,使得权函数的一阶导数处处光滑,增强了权函数突变范围内信息的可利用性。与IGGⅢ-ELM法、DBSCAN聚类算法、罗曼诺夫斯基准则和拉依达准则的实例处理结果对比表明:基于改进IGGⅢ-ELM法的混凝土坝变形监测数据粗差识别方法较其他4种方法粗差识别率更高、泛化能力更强、预测效果更好。Aiming at the problems of strong nonlinearity,difficulty in identifying and eliminating gross errors in concrete dam deformation monitoring data,an approach of gross error identification and a dam safety monitoring model for deformation monitoring data of concrete dams are proposed based on the improved IGGⅢ-ELM method,which combines the advantages of the improved IGGⅢmethod with the good robustness to outliers,the extreme learning machine(ELM)method with high efficiency in predicting data sequences and strong ability to handle nonlinear problems and the incremental ELM method with rapid optimal network structure seeking,the IGGⅢ-ELM method is improved by using a four-segment weight function containing two harmonic coefficients,making the first derivative of the weight function smooth everywhere, and enhancing the availability of information in the mutation range of the weightfunction. In the study case, the processing result of the improved IGG Ⅲ-ELM method was compared with the IGGⅢ-ELM method, DBSCAN clustering algorithm, Romanovsky criterion and Pauta criterion. The results show thatthe gross error identification and prediction method of concrete dam deformation monitoring data based on theimproved IGG Ⅲ-ELM method has a gross error identification rate, stronger generalization ability and betterprediction effect than the other four methods.

关 键 词：变形监测数据 混凝土坝 粗差识别 机器学习 变形预测 

分 类 号：TV698.1[水利工程—水利水电工程]