Title :
Coal Face Gas Emission Prediction Based on Support Vector Machine
Author :
Ning Yuncai ; Chen Xiang
Author_Institution :
Inst. of Manage., China Univ. of Min. & Technol., Beijing, China
Abstract :
Mine work face gas emission quantity is an important mine design basis, which also has important practical significance for guide mine design, ventilation and safety production. Mine gas emission quantity and work face multi factors have complex non-linear relationship. The paper built the work face gas emission prediction support vector machine (SVM) model. Based on data statistic of a mine work face gas emission, the paper used the model to predict gas emission. The result was accurate, which prove the model´s prediction for face gas is viable and effective.
Keywords :
mining industry; support vector machines; coal face gas emission prediction; data statistic; mine design; mine gas emission; safety production; support vector machine; ventilation; Artificial intelligence; Explosions; Neural networks; Prediction methods; Predictive models; Product safety; Production; Statistics; Support vector machines; Ventilation; gas emission quantity; index system; prediction; support vector machine;
Conference_Titel :
Artificial Intelligence and Computational Intelligence, 2009. AICI '09. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-3835-8
Electronic_ISBN :
978-0-7695-3816-7
DOI :
10.1109/AICI.2009.217