基于PCA-PNN的采空区多源指标危险性辨识  

Multi-source indexes risk identification of goaf based on PCA-PNN

作　　者：曹占华 袁海平[1] 李恒喆 CAO Zhanhua;YUAN Haiping;LI Hengzhe(School of Civil and Hydraulic Engineering,Hefei University of Technology,Hefei Anhui 230009,China)

机构地区：[1]合肥工业大学土木与水利工程学院,安徽合肥230009

出　　处：《中国安全生产科学技术》2022年第12期104-109,共6页Journal of Safety Science and Technology

基　　金：国家自然科学基金项目(51874112)。

摘　　要：为了提高采空区多源指标危险性辨识的预测精度,基于主成分分析(PCA)和概率神经网络(PNN),提出1种采空区多源指标危险性辨识方法。将影响华东某地区矿山采空区危险性辨识的9项因素作为主要影响因素,并以96个实测采空区为例进行分级。研究结果表明:与朴素贝叶斯、随机森林和AdaBoost 3种机器学习算法相比,PNN在测试集上表现更好,对实际工程具有良好的指导意义和应用价值。In order to improve the prediction accuracy for the multi-source index risk identification of goaf,based on the principal component analysis(PCA)and the probabilistic neural network(PNN),a kind of risk identification method of the multi-source indexes of goaf was proposed.9 factors affecting the risk identification of goaf in a region of east China were determined as the primary influencing factors,and 96 measured goafs were classified as examples.The results showed that compared with three machine learning algorithms of the Naive Bayes,the Random Forest and the AdaBoost,PNN performed more preferable on the test set,which has admirable guiding significance and application value for the practical engineering.

关 键 词：采空区 危险性评价 主成分分析 概率神经网络 机器学习 

分 类 号：X936[环境科学与工程—安全科学]