基于粗糙集与粒子群优化支持向量机的瓦斯突出预测模型

Gas outburst prediction based on rough set and particle swarm optimization support vector machine

针对煤矿开采中煤与瓦斯突出的预测问题,在综合分析瓦斯突出影响因素的基础上,利用粗糙集理论和支持向量机相结合的方法,选取煤厚变化、地质构造、煤坚固性系数、巷道采压、瓦斯变化、钻屑瓦斯解吸值等10个特征指标建立瓦斯突出预测决策表,并利用粗糙集理论中的属性约简算法剔除冗余信息,再使用粒子群算法优化支持向量机的参数,通过核函数将瓦斯突出主控因素映射到高维空间,拟合主控因素与瓦斯突出强度之间的非线性映射关系,建立了基于粗糙集理论和粒子群优化支持向量机的瓦斯突出预测模型.选用典型的瓦斯突出实例作为学习样本,以河南某矿的突出实例作为测试样本进行预测.实验结果表明,该模型能够满足煤与瓦斯突出预测的要求,预测结果与实际结果一致,准确率较高,具有较好的适应性.

In view of coal and gas outburst intensity forecast problems in coal mines,on the basis of comprehensive influence factors of gas outburst,a decision table of gas outburst intensity was established by employing the rough set theory and support vector machine,and selecting coal thickness variations,geological structures,coefficient of the solid coal,roadway pressure,gas change,gas desorption value of drilling chip,and ten main influence.Using the attribute reduction algorithm in rough set theory to eliminate redundant information,and particle swarm optimization to optimize parameters of Support Vector Machine,the main control factors of gas outburst were mapped to high-dimensional space through kernel function,and the nonlinear relationship between main control factors and intensity of gas outburst was fitted.A gas outburst prediction model based on rough set theory and particle swarm optimization support vector machine was established.A typical example of gas outburst was selected as a study sample,and a prominent example of a mine in Henan was used as a test sample for prediction.The experimental results show that the model can meet the requirements of gas outburst prediction,with the prediction results being consistent with the actual results.