Site selection of coal mine safety emergency material reserve center based on SBG XGBoost
The site selection optimization of coal mine safety emergency reserve center is an important foundation for pro-moting the construction of coal mine safety emergency response system.In order to improve the accuracy and reasonable-ness of coal mine safety emergency reserve center site selection,it is proposed to establish a machine learning combina-tion model for coal mine safety emergency reserve center site selection,integrating multi-source spatial data by using demographic,transportation,economic and natural factors to improve the accuracy and scientificity of coal mine safety emergency reserve center site selection.The accuracy and scientificity of coal mine safety emergency reserve center site selection are improved.Firstly,the ArcGIS is used to process multi-source spatial data through tasks such as fishing net di-vision,spatial linking and projection respectively,and the SMOTEENN algorithm is utilized to avoid the negative impact of data imbalance,so as to construct the dataset applicable to the analysis of machine learning model.Secondly,by com-paring and analyzing different machine learning algorithms,different feature selection methods and different parameter optimization methods,it is concluded that the XGBoost machine learning algorithm,the Boruta algorithm and genetic al-gorithm have better performance than other machine learning algorithms,feature selection methods and parameter optim-ization methods in the site selection analysis of coal mine safety and emergency reserve center.Therefore,based on the ad-vantages of each algorithm,this paper obtains a combined machine learning model for coal mine safety emergency re-serve center site selection.Finally,the SHAP analysis is introduced to analyze the influence degree and direction of differ-ent features to quantitatively assess the contribution of input data in decision-making and enhance the interpretability of the model.The results show that the combined model of coal mine safety emergency reserve center siting has an excellent performance,with 0.976,0.966,0.989,0.977,0.996 in accuracy,precision,recall,F1 value and Auc,respectively,which can provide a powerful support for siting decision-making,and the model interpretable analysis can also provide a scientif-ic reference for coal mine safety emergency reserve center siting.
coal mine safetyemergency material reserve centersite selectionmachine learningfeature selection
万方数据
维普
