NDLI: Support vector machine regression model of CBM content and application

Content Provider	IEEE Xplore Digital Library
Author	Tang Hong-wei Cheng Jian-yuan Wang Shi-dong
Copyright Year	2009
Description	Author affiliation: CCRI, Xi'an Branch, Shaanxi province, Xi'an city, 710054, China (Tang Hong-wei; Cheng Jian-yuan; Wang Shi-dong)
Abstract	In order to quantitatively predictive the content of the coalbed methane (CBM), we make use of the known parameters of the core tests data to establish the support vector machine regression model between the core data and coal-bed methane content. The model is based on the small sample size theory. Using the model, we can predict the volume of gas content. We choose the coal seam thickness, coal vitrinite reflectance value and coal ash 3 parameters as input feature vectors, and coal-bed methane content as the output vector of support vector machine regression prediction model. Application of the proposed model in Binchang mining shows that the prediction error between the measured results and prediction are small and meet the accuracy requirements.
Starting Page	99
Ending Page	102
File Size	83321
Page Count	4
File Format	PDF
ISBN	9781424447541
DOI	10.1109/ICICISYS.2009.5357929
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machine regression Error analysis Geology Artificial neural networks Predictive models Core tests Equations Support vector machines Reflectivity Coalbed methane content Ash Cities and towns Testing
Content Type	Text
Resource Type	Article

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