Abstract
Spontaneous combustion of coal mostly relates to the thickness of minimum float coal. Therefore, a new predictive model for spontaneous combustion of coal is presented based on support vector machines (SVM). Based on the intensity of the wind leak and the temperature of the coal mine measured in the gob of fully mechanized top-coal carving face, a predictive model using support vector machines is established. Then the minimum thickness of the mine layer is predicted using the model, and gotten early warning spontaneous combustion of coal. The practical examples show that the method outperforms the radial basis function networks on both the prediction precision and the generation ability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Deng, J., Chen, X., Zhai, X., Zhenmin Luo, J.: Grey Prediction Model of Spontaneous Combustion Stage of Coal. Journal of Xi’an University of Science and Technology 24, 406–416 (2004)
Song, Z., Cao, K., Baozheng Sun, J.: A Study on the Pattern Identification of Predicting Spontaneous Combustion of U-Form Mining Field, vol. 19, pp. 22–25 (1999)
Yuanbin Hou, J.: An RSNN-based Prediction Method for the Coal Mine Spontaneous Combustion. Information and Control 33, 93–96 (2004)
Vapnik, M.V.: Statistical Learning Theory. John Wiley and Sons, New York (1998)
Schölkoph, B., Smola, A.J., Bartlett, P.L.J.: New support vector algorithms. Neural Computation 12, 1207–1245 (2000)
Tay, F.E.H., Cao, L.J.: Modified Support Vector Machines in Financial Time Series Forecasting. Neuro-computing 48, 847–861 (2002)
Tay, F.E.H., Cao, L.J.: Graphics object to be inserted manually-Descending support vector machines for financial time series forecasting. Neural Processing Letters 15, 179–195 (2002)
Smola, A.J., Schoölkopf, R.B.: A Tutorial on Support Vector Regression, N.T.r.NC-TR-98-030, Editor. University of London, UK (1998)
David, V., Sánchez, A.J.: Advanced Support Vector Machines and Kernel Methods. Neuro-computing 55, 5–10 (2003)
Keerthi, S.S., Lin, C.J.: Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Computation 15, 1667–1689 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Du, J., Wang, L. (2009). A Model Based on SVM for Predicting Spontaneous Combustion of Coal. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_64
Download citation
DOI: https://doi.org/10.1007/978-3-642-02962-2_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02961-5
Online ISBN: 978-3-642-02962-2
eBook Packages: Computer ScienceComputer Science (R0)