PCA-RF Model for the Classification of Rock Mass Quality and Its Application

LIU Qiang; LI Xibing; LIANG Weizhang

doi:10.11872/j.issn.1005-2518.2018.01.049

Gold Science and Technology >

2018 , Vol. 26 >Issue 1: 49 - 55

DOI: https://doi.org/10.11872/j.issn.1005-2518.2018.01.049

PCA-RF Model for the Classification of Rock Mass Quality and Its Application

LIU Qiang ,
LI Xibing ,
LIANG Weizhang

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chool of Resources and Safety Engineering，Central South University，Changsha 410083，Hunan，China

Received date: 2016-12-12

Revised date: 2017-02-26

Online published: 2018-05-19

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Abstract

In order to determine the classification of rock mass quality more reasonably，PCA-RF classification model of rock mass quality was proposed which combined with principal component analysis and random forest algorithm.Five classification indexes were chosen which can fully reflect the rock mass quality category.The correlation analysis of indexes was calculated by principal component analysis，and three principal components were abtained by accumulated variance devoted rate，which can eliminate the correlation between each index and reduce the inputs of model.Then，the classification of rock mass quality was determined by random forest model.Twenty sets field data were chosen as training samples，and ten sets field data were chosen as testing samples.The generalization errors were estimated by cross-validation method.The results show that the classification results satisfyingly agree with the actual results at the average accuracy of 96.7%，and the probability distribution of classifications that can reflect the complexity of rock mass quality was calculated simultaneously，which can provide more detailed reference for engineering construction.

Key words： rock mass quality; principal component analysis; random forest; correlation of index; cross-validation; generalization errors; classification

Cite this article

LIU Qiang , LI Xibing , LIANG Weizhang . PCA-RF Model for the Classification of Rock Mass Quality and Its Application[J]. Gold Science and Technology, 2018 , 26(1) : 49 -55 . DOI: 10.11872/j.issn.1005-2518.2018.01.049

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