|本期目录/Table of Contents|

[1]邵良杉,马寒,温廷新.基于RF-ELM模型的边坡稳定性预测研究[J].中国安全生产科学技术,2015,11(3):93-98.[doi:10.11731/j.issn.1673-193x.2015.03.015]
　SHAO Liang-shan,MA Han,WEN Ting-xin.Study on slope stability prediction based on RF-ELM model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2015,11(3):93-98.[doi:10.11731/j.issn.1673-193x.2015.03.015]
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基于RF-ELM模型的边坡稳定性预测研究
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
11
期数:
2015年3期
页码:
93-98
栏目:
职业安全卫生管理与技术
出版日期:
2015-03-30

文章信息/Info

Title:
Study on slope stability prediction based on RF-ELM model
作者:
邵良杉; 马寒; 温廷新
(辽宁工程技术大学 系统工程研究所, 辽宁葫芦岛125105)
Author(s):
SHAO Liang-shan; MA Han; WEN Ting-xin
(System Engineering Institute, Liaoning Technical University, Huludao Liaoning 125105, China)
关键词:
边坡稳定性; 极限学习机; 旋转森林; 分类器集成
Keywords:
slope stability;  extreme learning machine;  rotation forest;  classifier integration
分类号:
X936
DOI:
10.11731/j.issn.1673-193x.2015.03.015
文献标志码:
A
摘要:
为实现对边坡稳定性的有效预测，将极限学习机算法与旋转森林算法相结合，并依据影响边坡稳定性的六项重要因素，建立了边坡稳定性预测的RF-ELM预测模型。该模型是以极限学习机算法为基分类器，以旋转森林算法为框架的集成学习模型，利用UCI数据库中三组数据集验证了该集成模型确实提高了ELM的预测性能。将RF-ELM模型应用于边坡稳定性的预测问题中，结合39组工程实例数据进行预测实验，结果表明该模型具有较高的预测精度，可有效的对边坡稳定性进行预测。
Abstract:
In order to predict the slope stability effectively, considering the six important influence factors of slope stability, a RF-ELM forecasting model of slope stability was established by combining extreme learning algorithm and rotation forest algorithm. This model is an integrated learning method, which uses extreme learning algorithm as base classifier and rotation forest algorithm as integration framework. A prediction test on 3 data sets of UCI database proved that the model can improve the prediction performance of ELM. By applying RF-ELM model in slope engineering, the prediction experiments were conducted on 39 groups of data in engineering cases. The results showed that RF-ELM model has a higher forecasting accuracy, and it is an effective model for predicting slope stability correctly.

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备注/Memo

备注/Memo:
国家自然科学基金资助项目(71371091);辽宁省高等学校杰出青年学者成长计划项目(LJQ2012027)
更新日期/Last Update: 2015-03-30