粗糙集属性约简在尾矿坝浸润线预测模型中的应用

在建立尾矿坝浸润线支持向量回归机（SVR）模型的过程中，预测精度低、计算时间长等问题较难解决，并且严重制约SVR模型的推广应用。为了解决以上问题，尝试引入粗糙集（RS）算法对训练样本的输入属性进行约简，同SVR算法共同建立浸润线预测模型。实例证明，RS-SVR模型有效降低了SVR模型在迭代时的计算难度，并使浸润线的预测精度得到了提高。由此可得，RS-SVR结合无论在理论上还是在实例应用中都具有可行性。

Application of Rough Set Attribute Reduction in the Prediction Model of Infiltration Route in Tailing Dam

During the establishment of the support vector regression（SVR) prediction model of infiltration route data in tailing dam，the prediction accuracy and the computational time are two difficult problems to control，which severely constrain the extensive application of the SVR model.To solve these two problems，the rough set（RS) algorithm is adopted to reduce the attribute for training samples，then the prediction model of infiltration route is established with the SVR algorithmThe cases proved that RS-SVR model effectively lowered the difficulty of SVR model iteration，and made the prediction accuracy improved.It can be seen that the RS-SVR is feasible not only in theory but in application.