Title :
Application of GA based Rough Set Theory in Forecast of Gas Outbursts
Author :
Xiaoming, Zhang ; Bingyu, Sun ; Rujing, Wang ; Jingdong, Tan
Author_Institution :
Inst. of Intell. Machines, Chinese Acad. of Sci., Hefei
Abstract :
Prediction of gas outbursts is an intricate task affected by many factors. A kind of non-parametric method-GA based rough set theory (GARS) is presented for gas outbursts forecast in coal mines. The rough set theory allows researchers to analyze gas outbursts accidents in multiple dimensions and to model gas outbursts. A real-world gas outbursts data set collected from Luling coal mine is used as a test data set to demonstrate the validity of the proposed approach. In the experiment, the model for gas outbursts forecast is built and experimental results show that the GARS is effective. GARS gives us a promising alternative for gas outbursts prediction.
Keywords :
data mining; genetic algorithms; industrial accidents; mining industry; occupational safety; rough set theory; Luling coal mine safety; attribute reduction; gas outburst accident forecasting; genetic algorithm; knowledge reduction; rough set theory; Accidents; Biomimetics; Computational intelligence; Intelligent robots; Knowledge representation; Predictive models; Set theory; Sun; Technology forecasting; Testing;
Conference_Titel :
Computational Intelligence and Design, 2008. ISCID '08. International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-0-7695-3311-7
DOI :
10.1109/ISCID.2008.81
