基于特征工程的XGBoost爆破块度预测研究

Study on Blasting Lumpiness by XGBoost Model based on Feature Engineering

露天矿山台阶爆破后矿岩的平均块度是衡量爆破质量的重要指标。早期研究主要依靠经验公式总结、岩体力学模型计算等方法,这些方法存在准确率不够、主观性强等缺点。近期,机器学习算法应用于块度预测,但基本通过专家经验选用固定的特征来进行预测且预测稳定性不足,泛化能力差。针对以上缺点,提出一种基于特征工程的极端梯度提升树(XGBoost)爆破块度预测模型。以太原袁家村铁矿为研究区,采集近半年的爆破数据作为原始数据,综合考虑影响平均块度的各方面因素。首先使用随机森林(RF)的袋外估计和互信息(MI)两种方法分别进行特征选择,其次将不同方法选择的特征子集集成并利用特征之间的互信息进行去冗余,最后以MSE的值为评价指标选出最优特征子集表征爆破,完成基于数据驱动的特征选择。更进一步,在最优特征子集上采用XGBoost算法进行块度预测,通过均方误差(MSE)、平均绝对误差(MAE)两个指标构成模型的评价体系将文章所提方法与其他传统机器学习算法进行对比。对比结果表明:文章提出方法比传统机器学习算法的预测准确率更高,可以为爆破的管理与控制提供科学指导。

The average lumpiness of ore rock is an important index to measure the blasting quality.The early research mainly relies on empirical formula summary,rock mechanics model calculation,which have shortcomings such as insufficient accuracy and strong subjectivity.Recently,,machine learning algorithm is applied for prediction,but still have problems such as empirical feature selection,insufficient model prediction stability,and poor generalization ability for the prediction of blasting material fragmentation.Aiming at above shortcomings,an extreme Gradient Boosting(xgboost)blasting fragmentation prediction model based on Feature Engineering is proposed.Taking Yuanjiacun Iron Mine in Taiyuan as the research area,engineering data are collected,Random Forest(RF)and Mutual Information(MI)are used for feature selection respectively,and the two feature subsets are integrated to obtain the best feature subset based on the value of MSE.XGBoost is used to predict the block size on the optimal feature subset,and the evaluation system is composed of two indexes:Mean Square Error(MSE)and Mean Absolute Error(MAE).The proposed method is compared with other traditional machine learning algorithms,and the results show that it is better than others.Furthermore,it can provide scientific guidance for the management and control of blasting.