中国农村水利水电Issue(4)：96-100,5.DOI:10.12396/znsd.2500767
基于CPO-HKELM的库岸滑坡位移预测
Prediction of Reservoir Bank Landslide Displacement Based on CPO-HKELM
付浩雁 1吕慧 1王郑 1郭庆霞1
作者信息
- 1. 南京市水利规划设计院股份有限公司,江苏 南京 210000
- 折叠
摘要
Abstract
In view of the difficulty of deformation prediction due to the complex operating environment,random load and strong nonlinearity of the bank slope of the library,Kernel Extreme Learning Machine(KELM)is proposed to model the monitoring data and hybrid kernel function is introduced to enhance the model mapping ability.In order to solve the problem that the mapping ability of the Hybrid Kernel Extreme Learning Machine(HKELM)is affected by hyperparameters,a combined prediction model named CPO-HKELM is constructed by applying the Crested Porcupine Optimizer(CPO)to optimize the nuclear parameters and penalty factors of HKELM.Taking the landslide of a certain reservoir bank as the research object,the H04 monitoring data of the landslide was modeled.In order to verify the feasibility and superiority of the proposed model,CPO-KELM,CPO-ELM and CPO-BP models were introduced for comparative analysis.The results show that the prediction accuracy of the proposed CPO-HKELM is obviously higher than the other two models,and the error is smaller,which has a good application prospect in landslide displacement prediction.关键词
库岸滑坡/位移预测/混合核极限学习机/冠豪猪算法/CPO-HKELMKey words
reservoir landslide/displacement prediction/hybrid kernel extreme learning machine/crested porcupine algorithm/CPO-HKELM分类
天文与地球科学引用本文复制引用
付浩雁,吕慧,王郑,郭庆霞..基于CPO-HKELM的库岸滑坡位移预测[J].中国农村水利水电,2026,(4):96-100,5.
