基于PCA-GA-RF的矿井突水水源快速识别模型OA北大核心CSTPCD
Mine water inrush source identification model based on PCA-GA-RF
矿井突水已成为影响矿山安全生产的主要危害之一,快速准确识别突水水源类型是矿井突水灾害治理的关键步骤.提出了 1 种基于PCA-GA-RF的矿井突水水源识别模型;基于安徽省颍上县谢桥煤矿的 88 组水样实测数据,遵循分层随机抽样的原则,按照 7∶3 的比例将其分为 62 组训练样本和 26 组预测样本,经PCA提取 4 个主成分,构建PCA-GA-RF模型,并与PCA-RF、PCA-ABC-RF和PCA-FA-RF模型对比.结果表明:PCA-GA-RF模型判别结果准确率为 96.1538%,与其他模型相比准确率、精确率、召回率和F1 值(精确召回率)最高,具有优越性.
Mine sudden water has become one of the main hazards affecting the safety production of mines,and rapid and accurate identification of the type of sudden water source is a key step in the management of mine sudden water disaster,so a PCA-GA-RF-based mine sudden water source identification model is proposed.Based on the measured data of 88 groups of water samples from Xieqiao Coal Mine in Yingshang County,Anhui Province,and following the principle of stratified random sampling,it was divided into 62 groups of training samples and 26 groups of prediction samples according to the ratio of 7:3,and the four principal compon-ents were extracted by PCA to construct the PCA-GA-RF model,and compare it with the PCA-RF,PCA-ABC-RF and PCA-FA-RF models.The results show that the PCA-GA-RF model discriminates the results with an accuracy of 96.1538%,which is superior with the highest accuracy,precision,recall and F1 value compared with other models.
肖观红;鲁海峰
安徽理工大学 地球与环境学院,安徽 淮南 232001
矿山工程
矿井突水水源识别主成分分析(PCA)随机森林(RF)遗传算法(GA)
mine water inrushwater source identificationprincipal component analysis(PCA)random forest(RF)genetic al-gorithm(GA)
《煤矿安全》 2024 (006)
184-191 / 8
国家自然科学基金资助项目(41977253);安徽理工大学研究生创新基金资助项目(2023cx2007)
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