|本期目录/Table of Contents|

[1]李胜,韩永亮.基于MFOA-SVR露天矿边坡变形量预测研究[J].中国安全生产科学技术,2015,11(1):11-16.[doi:10.11731/j.issn.1673-193x.2015.01.002]
 LI Sheng,HAN Yong-liang.Research on forecasting of slope deformation in open-pit mine based on MFOA-SVR[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2015,11(1):11-16.[doi:10.11731/j.issn.1673-193x.2015.01.002]
点击复制

基于MFOA-SVR露天矿边坡变形量预测研究
分享到:

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
11
期数:
2015年1期
页码:
11-16
栏目:
学术论著
出版日期:
2015-01-30

文章信息/Info

Title:
Research on forecasting of slope deformation in open-pit mine based on MFOA-SVR
作者:
李胜韩永亮
(辽宁工程技术大学 矿业学院,辽宁阜新123000)
Author(s):
LI Sheng HAN Yong-liang
(College of Mining Engineering, Liaoning Technical University, Fuxin Liaoning 123000, China)
关键词:
边坡变形支持向量机回归(SVR)修正的果蝇优化算法(MFOA)仿真预测
Keywords:
slope deformation support vector machine regression(SVR) modified fruit fly optimization algorithm (MFOA) simulation and forecast
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2015.01.002
文献标志码:
A
摘要:
为实现边坡危险性及时预警预报,以露天矿边坡变形量为研究对象,提出采用七项影响指标作为边坡位移变形量的响应参数,建立支持向量机回归预测模型(SVR)。引入修正的果蝇优化算法(MFOA)对模型参数进行优化,构建基于MFOA-SVR露天矿边坡变形量协同预测模型,并以实际监测数据进行模型仿真预测。结果表明:该模型平均绝对误差为0.9167mm,平均相对误差为4.2737%,较其他模型预测精度高,综合性能好,将其运用于露天矿边坡变形量预测研究具有较好的适用性和可靠性。
Abstract:
In order to achieve the timely warning and forecasting of slope hazard, taking the slope deformation of open-pit mine as study object, it was proposed to establish the support vector machine regression model (SVR) by adopting 7 influence indexes as the response parameters of slope displacement and deformation. The modified fruit fly optimization algorithm (MFOA) was introduced to optimize the parameters of model. The collaborative forecasting model of slope deformation in open-pit mine based on MFOA-SVR was established, and the simulation and forecast of the model were conducted by practical monitoring data. The results showed that the mean absolute error of the model is 0.9167 mm, and the mean relative error is 4.2737%, which had higher precision and better comprehensive performance than other models. It has a good applicability and reliability when used in forecasting of slope deformation in open-pit mine.

参考文献/References:

[1]付士根. 基于模糊随机可靠性的边坡稳定性评价[J]. 中国安全生产科学技术,2012,8(8):98-101 FU Shi-gen. Stability assessment of opencast mines slope based on fuzzy stochastic reliability [J]. Journal of Safety Science and Technology,2012,8(8):98-101
[2]何习平,华锡生,何秀凤.加权多点灰色模型在高边坡变形预测中的应用[J].岩土力学,2007, 28 (6):1187-1191 HE Xi-ping, HUA Xi-sheng, HE Xiu-feng. Weighted multi-point grey model and its application to high rock slope deformation forecast[J]. Rock and Soil Mechanics, 2007, 28 (6):1187-1191
[3]文海家,赵亮,李鑫.边坡变形的GPS-ANN综合分析方法[J].重庆大学学报,2010,33(2):79-82 WEN Hai-jia, ZHAO Liang, Li Xin. Combination of GPS monitoring and ANN prediction for slope deformation[J]. Journal of Chongqing University, 2010,33(2):79-82
[4]陆付民,王尚庆,李劲,等.顾及地下水位因子的卡尔曼滤波模型在滑坡变形预测中的应用[J].武汉大学学报:信息科学版,2010,35(10) : 1184-1187 LU Fu-min, WANG Shang-qing, LI Jin, et al.Application of Kalman filter model considering groundwater level factors to landslide deformation forecast [J].Geomatics and Information Science of Wuhan University, 2010,35(10) : 1184-1187
[5]张安兵,高井祥,刘新侠,等.边坡变形时序非线性判定及混沌预测研究[J].中国安全科学学报,2008,18(4):55-60 ZHANG An-bing, GAO Jing-xiang, LIU Xin-xia, et al.Nonlinear test and chaotic prediction of slope deformation sequences[J]. China Safety Science Journal, 2008,18(4):55-60
[6]陈祖云,张桂珍,邬长福,等.基于支持向量机的边坡稳定性预测研究[J].中国安全生产科学技术,2009,5(4):101-105 CHEN Zu-yen, ZHANG Gui-zhen, WU Chang-fu, et al.Study of prediction of slope stability based on support vector machines[J]. Journal of Safety Science and Technology, 2009,5(4):101-105
[7]李凤明,李宏艳,孙维吉.基于支持向量机的露天矿边坡地表变形预测[J].煤炭学报, 2008,33(5):492-495 LI Feng-ming, LI Hong-yan, SUN Wei-ji. Forecast of surface deformation of slope of strip mine based on support vector machine[J]. Journal of China Coal Society,2008,33(5):492-495
[8]张冬梅,徐卫亚,赵博.基于COA-LSSVM模型的边坡位移时序预测[J].水电能源科学,2014,32(5):105-108 ZHANG Dong-mei, XU Wei-ya, ZHAO Bo. Forecasting of Slope Displacement Time-series Based on COA-LSSVM Model[J]. Water Resources and Power, 2014,32(5):105-108
[9]史峰,王辉,郁磊,等.MATLAB智能算法30个案例分析[M].北京:北京航空航天大学出版社,2011:280-281
[10]颜七笙,王士同.基于PSO-SVR的岩质边坡稳定性评价模型[J].计算机工程与应用,2011,47(16):235-238 YAN Qi-sheng, WANG Shi-tong. Stability evaluation model of rock mass slope based on PSO-SVR[J]. Computer Engineering and Applications, 2011,47(16):235-238
[11]张豪,罗亦泳. 基于人工免疫算法的边坡稳定性预测模型[J]. 煤炭学报, 2012,37(6):911-917 ZHANG Hao, LUO Yi-yong. Prediction model for slope stability based on artificial immune algorithm[J]. Journal of China Coal Society,2012,37(6):911-917

相似文献/References:

备注/Memo

备注/Memo:
国家自然科学基金项目(51004063);辽宁省高等学校优秀人才支持计划项目(LJQ2011029)
更新日期/Last Update: 2015-02-03