Application of Bayes Discriminant Analysis in Mine Geological Environment Safety Evaluation
Li Yun
Abstract
Li Yun
Abstract
From the actual characteristics of mine geological environment,Bayes discriminant analysis method is used to evaluate the geologic environment problem of mine.13 specific items affecting regional geological conditions,state of environment,mineral resources developing and planning,ecological recovery and control,etc.are selected as the assessment indexes,a Bayes discriminant analysis model for mine geological environment assessment is established based on large quantities of learning samples obtained by measurement and the prior probability obtained according to the proportion of each sample.Then,the model is used to predict the evaluation samples,and the result is verified with an error rate of zero and a good agreement with actuality based on back estimation method.It is concluded that Bayes discriminant analysis model possesses a good stability of structure and a high accuracy of distinguishing,which can be used for assessing mine geological environment.
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From the actual characteristics of mine geological environment,Bayes discriminant analysis method is used to evaluate the geologic environment problem of mine.13 specific items affecting regional geological conditions,state of environment,mineral resources developing and planning,ecological recovery and control,etc.are selected as the assessment indexes,a Bayes discriminant analysis model for mine geological environment assessment is established based on large quantities of learning samples obtained by measurement and the prior probability obtained according to the proportion of each sample.Then,the model is used to predict the evaluation samples,and the result is verified with an error rate of zero and a good agreement with actuality based on back estimation method.It is concluded that Bayes discriminant analysis model possesses a good stability of structure and a high accuracy of distinguishing,which can be used for assessing mine geological environment.
Key concepts: Bayes' theorem, Linear discriminant analysis, Discriminant, Naive Bayes classifier, Sample (material), Data mining, Statistics, Stability (learning theory)