|本期目录/Table of Contents|

[1]华国威,娄彦彬,王世杰,等.基于PCA-BBO-SVM的尾矿坝变形预测模型与性能验证研究*[J].中国安全生产科学技术,2022,18(9):20-26.[doi:10.11731/j.issn.1673-193x.2022.09.003]
　HUA Guowei,LOU Yanbin,WANG Shijie,et al.Prediction model of tailings dam deformation based on PCA-BBO-SVM and its performance verification[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2022,18(9):20-26.[doi:10.11731/j.issn.1673-193x.2022.09.003]
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基于PCA-BBO-SVM的尾矿坝变形预测模型与性能验证研究*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
18
期数:
2022年9期
页码:
20-26
栏目:
学术论著
出版日期:
2022-09-30

文章信息/Info

Title:
Prediction model of tailings dam deformation based on PCA-BBO-SVM and its performance verification
文章编号:
1673-193X(2022)-09-0020-07
作者:
华国威; 娄彦彬; 王世杰; 胡少华
(1.武汉理工大学 安全科学与应急管理学院，湖北 武汉 430070；
2.河南省电力勘测设计院,河南 郑州 450007;
3.国家大坝安全工程技术研究中心，湖北 武汉 430010）
Author(s):
HUA Guowei;  LOU Yanbin;  WANG Shijie;  HU Shaohua
(1.School of Safety Science and Emergency Management,Wuhan University of Technology,Wuhan Hubei 430070,China;
2.Henan Electric Power Survey & Design Institute,Zhengzhou Henan 450007,China；
3.National Research Center for Dam Safety Engineering Technology,Wuhan Hubei 430010,China)
关键词:
尾矿坝; 变形预测; PCA-BBO-SVM; 性能验证
Keywords:
tailings dam;  deformation prediction;  PCA-BBO-SVM;  performance verification
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2022.09.003
文献标志码:
A
摘要:
为准确预测尾矿坝变形趋势，通过主成分分析法（PCA）对尾矿坝变形影响因子进行优选，基于生物地理学优化算法（BBO）对支持向量机（SVM）参数进行寻优，建立PCA-BBO-SVM尾矿坝变形预测模型，并以杨家湾尾矿坝为例对模型性能进行验证。研究结果表明：PCA-BBO-SVM模型在4个测点的RMSE为0.139 6，0.274 2，0.317 0，0.530 6；MAE为0.112 5，0.213 5，0.269 0，0.412 9；MAPE为0.525 0%，0.692 3%，2.621 2%，1.311 2%；预测精度及对局部波动的预测能力均高于BP、GS-SVM、GA-SVM和PSO-SVM模型，研究结果可为尾矿坝变形预测提供模型支撑。
Abstract:
In order to accurately predict the deformation trend of tailings dam,the principal component analysis method (PCA) was used to optimally screen out the influencing factors of tailings dam deformation,and the parameters of support vector machine (SVM) were optimized based on the biogeographic optimization algorithm (BBO).A PCA-BBO-SVM prediction model of tailings dam deformation was established,and the Yangjiawan tailings dam was taken as an example to verify the performance of the model.The results showed that the RMSE of the PCA-BBO-SVM model at four measuring points were 0.139 6,0.274 2,0.317 0 and 0.530 6,the MAE were 0.112 5,0.213 5,0.269 0 and 0.412 9,and the MAPE were 0.525 0%,0.692 3%,2.621 2% and 1.311 2%,respectively.The prediction accuracy and the ability to predict local fluctuation were higher than those of BP,GS-SVM,GA-SVM and PSO-SVM models,and it can provide technical support for the deformation prediction of the tailings dam.

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备注/Memo

备注/Memo:
收稿日期： 2021-08-11；网络首发日期： 2022-07-04
* 基金项目： 国家自然科学基金项目(51979208)；国家“十三五”重点研发计划项目(2017YFC0804600)；国家大坝安全工程技术研究中心开放基金项目(CX2019B014)
作者简介： 华国威，硕士研究生，主要研究方向为工程安全与应急管理。
通信作者： 胡少华，博士，副教授，主要研究方向为工程安全与应急管理。
更新日期/Last Update: 2022-10-14