Research on the Fitting and Predicting Models for Coal Bed Methane Dynamic Productivity of Coal Mine Area

Abstract:

Firstly a GM(1,1) is built to get the dynamic base line for the coal bed methane dynamic productivity of coal mine area. Secondly on the basis of the GM(1,1), Markov chain is applied to achieve state transition probability matrix. Thirdly the coal bed methane dynamic productivity of coal mine area interval is forecasted and analyzed in the form of probability by the system state classification, the calculation of the residue between true value and model fitting value and the standardization of deviation of the residue. It's proved in theory and practice that the forecast results not only are more reliable but also can help the decision maker with grasping the coal bed methane dynamic productivity of coal mine area development tendency in general and making proper decision. Results show that the Grey Markov Model has higher accuracy than that of GM(1,1) model.

• Periodical: Advanced Materials Research (Volumes 634-638)
• Pages: 819-824
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.634-638.819
• Online since: January 2013
• Authors: Zhi Gang Zhang
• Keywords: Coalbed Methane (CBM), Grey Markov Model, Output Forecasting

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