基于遗传算法优化XGBoost的煤矿安全预警方法研究

Research on coal early warning method of mine safety based on genetic algorithm optimized XGBoost

煤矿安全预警对于减少生产安全事故的发生和降低企业损失具有重要作用。为此,该研究基于遗传算法(Genetic Algorithm,GA)与XGBoost算法设计了一种露天煤矿的边坡稳定性预测方法。为提高边坡稳定性预测精度,利用遗传算法优化XGBoost算法的超参数,根据最优参数组合建立GA-XGBoost模型进行预测研究。实验结果表明,该方法能有效地预测露天煤矿的边坡位移,与传统的预测方法相比,其决定系数提高了5%以上,均方误差降低了50%以上,更适合应用到露天煤矿的边坡稳定性预测工作中。

Coal mine safety early warning plays an important role in reducing the occurrence of production safety accidents and reducing enterprise loss.Therefore,a slope stability prediction method was designed based on the Genetic Algorithm(GA)and XGBoost algorithm.In order to improve the prediction accuracy of slope stability,the genetic algorithm is used to optimize the hyperparameters of the XGBoost algorithm and build the GA-XGBoost model according to the optimal parameter combination.The experimental results show that this method can effectively predict the slope displacement of open⁃pit coal mines.Compared with the traditional prediction method,the determination coefficient is increased by more than 5%,and the mean square error is reduced by more than 50%,which is more suitable for the slope stability prediction of open⁃pit coal mines.