基于支持向量机的导水裂隙带发育高度预测  

Forecast on Development Height of Water Flowing Fractured Zone Based on Support Vector Machine

作　　者：谭希鹏[1] 施龙青[1] 王娟[1] 季小凯 景行[1] 邢同菊 刘云[1] 

机构地区：[1]山东科技大学地质科学与工程学院,山东青岛266590

出　　处：《煤矿安全》2014年第8期46-49,共4页Safety in Coal Mines

基　　金：教育部高等学校博士学科点专项科研基金资助项目(20133718110004);青岛经济技术开发区重点科技发展计划项目(2013-1-62);山东省自然科学基金资助项目(ZR2011EEZ002);山东科技大学科研创新团队支持计划资助项目(2012KYTD101)

摘　　要：在大量实测数据的基础上探究了经验公式法的局限性并基于理论层面分析了BP神经网格法局限性。探究了支持向量机计算导水裂隙带发育高度的优势,根据43个收集到的顶板裂隙带发育高度实测数据,依照参数获取简便,与导水裂隙带发育高度相关性相对较高的原则,以采高、顶板覆岩强度以及开采方式作为特征因子,建立导水裂隙带发育高度回归模型。利用模型预测了谢桥1121工作面及白庄7507工作面导水裂隙带发育高度。结果表明:运用SVM回归模型预测导水裂隙带发育高度具有较好的可行性。Based on many measured data, the limitation of empirical formula was studied. Furthermore, boundedness of BP neural net- works has discussed in the tier of theory. The superiority of using support vector machine to forecast development height of water flowing fractured zone is discussed, According to 43 collected roof fracture development height measured data, in accordance with the princi- ples of getting simple parameters and having relatively high correlation with development height of water flowing fractured zone, taking mining height, roof overburden strength and mining methods as characteristic factor, the article establishes a high degree regression model for development height of water flowing fractured zone. The model is used to predict development height of water flowing fractured zone in Xieqiao 1121 and Baizhuang 7507 working face. The results show that the use of SVM regression models to predict the development height of water flowing fractured zone has good feasibility.

关 键 词：支持向量机 导水裂隙带 经验公式 BP神经网格 预测 发育高度 

分 类 号：TD742.2[矿业工程—矿井通风与安全]