Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration.
基于虚拟战场对战场态势信息的需求,构建了Web模式的仿真想定编辑与管理系统,该系统包括仿真想定的输入,管理,编辑,标绘,输出等功能模块。在地理信息系统(Geographic Information System,GIS)和军标库等的支持下,用户可以根据具体的仿真需求,通过对仿真想定进行可视化编辑,简单灵活地实现对战场初始态势信息的输入。同时系统加强对已有的仿真想定的统一管理,建立仿真想定库,便于用户获取已有的仿真想定,大大提高其重用率,减少了不必要的重复劳动。 相似文献