Prediction of Top Coal Caving Ability Based on Support Vector Machine

Yong Kui Shi; Ting Ting Sun; Jian Hao; Shuai Hao

doi:10.4028/www.scientific.net/AMM.217-219.2702

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 217-219Prediction of Top Coal Caving Ability Based on...

Prediction of Top Coal Caving Ability Based on Support Vector Machine

Abstract:

This paper selected the factors as the coal mining depth, coal seam thickness, the dips, partings, main roof of the top coal caving as indicators, used the categorical data of the steep seam from domestic mining area as training samples for training. Based on one to one classification of SVM, the top coal caving ability prediction model was established. The example results show that the model prediction method is feasible, the prediction results have very high accuracy and reliability, and has certain promotional value.

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Periodical:

Applied Mechanics and Materials (Volumes 217-219)

Pages:

2702-2705

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.217-219.2702

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Online since:

November 2012

Authors:

Yong Kui Shi, Ting Ting Sun, Jian Hao, Shuai Hao

Keywords:

MATLAB, Prediction, Support Vector Machine (SVM), Top Coal Caving Ability

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References

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