Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing...Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing two practical models to predict pillar stability status.For this purpose,two robust models were developed using a database including 236 case histories from seven underground hard rock mines,based on gene expression programming(GEP)and decision tree-support vector machine(DT-SVM)hybrid algorithms.The performance of the developed models was evaluated based on four common statistical criteria(sensitivity,specificity,Matthews correlation coefficient,and accuracy),receiver operating characteristic(ROC)curve,and testing data sets.The results showed that the GEP and DT-SVM models performed exceptionally well in assessing pillar stability,showing a high level of accuracy.The DT-SVM model,in particular,outperformed the GEP model(accuracy of 0.914,sensitivity of 0.842,specificity of 0.929,Matthews correlation coefficient of 0.767,and area under the ROC of 0.897 for the test data set).Furthermore,upon comparing the developed models with the previous ones,it was revealed that both models can effectively determine the condition of pillar stability with low uncertainty and acceptable accuracy.This suggests that these models could serve as dependable tools for project managers,aiding in the evaluation of pillar stability during the design and operational phases of mining projects,despite the inherent challenges in this domain.展开更多
The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics h...The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.展开更多
文摘Assessing the stability of pillars in underground mines(especially in deep underground mines)is a critical concern during both the design and the operational phases of a project.This study mainly focuses on developing two practical models to predict pillar stability status.For this purpose,two robust models were developed using a database including 236 case histories from seven underground hard rock mines,based on gene expression programming(GEP)and decision tree-support vector machine(DT-SVM)hybrid algorithms.The performance of the developed models was evaluated based on four common statistical criteria(sensitivity,specificity,Matthews correlation coefficient,and accuracy),receiver operating characteristic(ROC)curve,and testing data sets.The results showed that the GEP and DT-SVM models performed exceptionally well in assessing pillar stability,showing a high level of accuracy.The DT-SVM model,in particular,outperformed the GEP model(accuracy of 0.914,sensitivity of 0.842,specificity of 0.929,Matthews correlation coefficient of 0.767,and area under the ROC of 0.897 for the test data set).Furthermore,upon comparing the developed models with the previous ones,it was revealed that both models can effectively determine the condition of pillar stability with low uncertainty and acceptable accuracy.This suggests that these models could serve as dependable tools for project managers,aiding in the evaluation of pillar stability during the design and operational phases of mining projects,despite the inherent challenges in this domain.
基金supported by the National Natural Science Foundation of China Key Project under Grant No.70933003the National Natural Science Foundation of China under Grant Nos.70871109 and 71203247
文摘The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.
