| 基于PSO-DSRVM的边坡变形预测 |
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| 引用本文： | 袁于思,冯小鹏,李勇,易灿灿.基于PSO-DSRVM的边坡变形预测[J].中国地质灾害与防治学报,2023,34(1):1-7. |
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| 作者姓名： | 袁于思 冯小鹏 李勇 易灿灿 |
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| 作者单位： | 1.中铁武汉电气化局集团第一工程有限公司，湖北 武汉 430074 |
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| 基金项目： | 国家自然科学基金项目（51805382）；湖北省安全生产专项资金科技项目（KJZX202007003） |
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| 摘 要： | 为了建立高精度的边坡位移预测模型，文章采用基于粒子群优化(PSO)的双稀疏相关向量机(DSRVM)建立边坡稳定性和影响因素之间的非线性关系。双稀疏相关向量机是在变分和相关向量机(RVM)框架下提出的一种多核组合优化的方法，相比于RVM和其他多核学习方法，DSRVM不仅有更少的训练时间，并且能够得到更高的预测精度。由于DSRVM的核参数对预测效果的影响较大，文章采用粒子群算法实现多个核参数的优化选取并应用于边坡位移预测。最后将本文提出的基于粒子群优化的双稀疏相关向量机(PSO-DSRVM)预测结果与极限学习机(ELM)和小波神经网络(WNN)预测结果进行对比，通过均方根误差(RMSE)、复相关系数(R2)和平均相对预测误差(ARPE)进行评价，验证了PSO-DSRVM模型在边坡变形预测上的可行性。
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| 关 键 词： | 边坡稳定 位移预测 粒子群优化算法 双稀疏相关向量机 |
| 收稿时间： | 2021-12-27 |
Prediction of mine slope deformation based on PSO-DSRVM |
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| Institution: | 1.China Railway Wuhan Electrification Bureau Group Co.Ltd.,Wuhan, Hubei 430074, China2.Wuhan University of Science and Technology, Wuhan, Hubei 430081, China |
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| Abstract: | In order to establish a high-precision prediction model of mine slope displacement, Doubly Sparse Relevance Vector Machine （DSRVM） based on Particle Swarm Optimization （PSO） was used to establish the nonlinear relationship between slope stability and influencing factors in this paper. DSRVM was a multi-core combinatorial optimization method, which was proposed under the framework of variational and Relevance Vector Machines （RVM）. Compared with RVM and other multiple-kernel learning methods, DSRVM not only had less training time, but also can obtained higher prediction accuracy. Aiming at the influence of the parameter’s selection of DSRVM on the final prediction effect, the optimal multiple kernel parameters was determined by PSO algorithm to be used in the mine slope displacement prediction. Compared the computational results of DSRVM with Extreme Learning Machine （ELM） and Wavelet Neural Network （WNN）, the feasibility of PSO-DSRVM in slope deformation prediction was verified by the evaluation indicators such as RMSE, R2 and ARPE. |
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