| 基于RS-BPNN理论的边坡稳定性预测及应用 |
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| 引用本文： | 李国辉,刘永,招国栋,彭洁.基于RS-BPNN理论的边坡稳定性预测及应用[J].南华大学学报(自然科学版),2015,29(3):122-128. |
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| 作者姓名： | 李国辉 刘永 招国栋 彭洁 |
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| 作者单位： | 南华大学 环境保护与安全工程学院,湖南 衡阳 421001,南华大学 研究生院,湖南 衡阳 421001,南华大学 研究生院,湖南 衡阳 421001,南华大学 环境保护与安全工程学院,湖南 衡阳 421001 |
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| 基金项目： | 环境保护部科研基金资助项目(监管1409&监管1509)；湖南省研究生创新基金资助项目(2015SCX25) |
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| 摘 要： | 从文献资料中收集并整理了45组各类危险边坡数据实例,结合粗糙集理论的数据挖掘功能和BP神经网络理论的非线性映射功能,建立了基于粗糙集-BP神经网络(RS-BPNN)理论的边坡稳定性预测模型.利用粗糙集对离散化后的数据进行了属性约简,利用神经网络对约简前后的数据进行了网络训练和仿真,并对其中五组边坡的安全系数和稳定状态进行了预测.结果表明,未经约简的BP网络安全系数预测的平均误差率为14.51%,约简后的RS-BP网络预测的平均误差率为7.24%,且经过粗糙集约简后边坡的预测状态与边坡的实际状态更加吻合.
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| 关 键 词： | 边坡稳定性 粗糙集 BP神经网络 属性约简 预测 |
| 收稿时间： | 2015/3/22 0:00:00 |
The Prediction and Application of Slope Stability Based on RS-BPNN |
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| LI Guo-hui,LIU Yong,ZHAO Guo-dong and PENG Jie.The Prediction and Application of Slope Stability Based on RS-BPNN[J].Journal of Nanhua University:Science and Technology,2015,29(3):122-128. |
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| Authors: | LI Guo-hui LIU Yong ZHAO Guo-dong and PENG Jie |
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| Institution: | School of Environmental Protection and Safety Engineering,University of South China,Hengyang, Hunan 421001,China,The Graduate School,University of South China,Hengyang,Hunan 421001,China,The Graduate School,University of South China,Hengyang,Hunan 421001,China and School of Environmental Protection and Safety Engineering,University of South China,Hengyang, Hunan 421001,China |
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| Abstract: | Forty-five sets of various data of dangerous slope were collected and arranged from literature in this paper,and prediction model of slope stability based on RS-BPNN Theory was established by using data mining functions of Rough Set Theory and nonlinear mapping function of BP Neural Network Theory.BP Neural Network was employed to train and simulate the discrete data,before and after that had been reduced by using attribute reduction based on Rough Set.The results show that the average error rate of safety coefficient prediction of BP Network and RS-BP Network is 14.51% and 7.24% with or without attribute reduction,respectively,and the predicted state of the slope is more consistent with the actual state through the use of attribute reduction based on Rough Set. |
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| Keywords: | slope stability Rough Set BP neural network attribute reduction prediction |
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