Junhui Wang1, Wanzi Yan1, Zhijun Wan1,*, Yi Wang2,*, Jiakun Lv1, Aiping Zhou3
CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.3, pp. 1135-1157, 2020, DOI:10.32604/cmes.2020.014313
Abstract Precise recovery of Coalbed Methane (CBM) based on transparent
reconstruction of geological conditions is a branch of intelligent mining.
The process of permeability reconstruction, ranging from data perception
to real-time data visualization, is applicable to disaster risk warning and
intelligent decision-making on gas drainage. In this study, a machine learning
method integrating the Random Forest (RF) and the Genetic Algorithm
(GA) was established for permeability prediction in the Xishan Coalfield
based on Uniaxial Compressive Strength (UCS), effective stress, temperature
and gas pressure. A total of 50 sets of data collected by a self-developed
apparatus were used to generate datasets for… More >
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