煤矿安全2025，Vol.56Issue(7)：175-184,10.DOI:10.13347/j.cnki.mkaq.20241577
彬长矿区煤层采动导水裂隙带高度RF-BP模型预测对比研究
Comparative study on RF-BP model prediction of mining water-conducting fracture zone height in Binchang Coal Mine
摘要
Abstract
The occurrence conditions of coal seams in western Huanglong Jurassic coalfield are generally thick,of which the aver-age thickness of coal seams in Binchang Mining Area is greater than 5 m,and the thickest coal seam can reach 14 m,and the fully mechanized caving technology is often adopted,resulting in large thickness and unclear development law in the water-conducting fracture zone of coal seam roof,and high water inflow in the mine,which seriously affects the safety production in the mining area.In order to study the development height of seam roof water-conducting fracture zone caused by disturbed overlying rock mining in Binchang Coal Mine,seven influencing factors such as the thickness of coal seam,seam buried depth,roof overlying rock lithology,roof structure characteristics,mining speed,the length of working face and mining technology were first selected.Firstly,the weight of the above influencing factors is calculated by AHP,and it is found that the weight of the two influencing factors,the thickness of coal seam and the length of working face,is relatively large.The collected data is interpolated by Matlab to make the data distribu-tion smoother.Back propagation neural network,genetic algorithm and particle swarm optimization were used to optimize BP neur-al network and random forest algorithm to carry out regression fitting for the interpolated data.It is found that the four methods have better fitting effect on the original data,and random forest RF has higher fitting accuracy than the other models.The root mean square errors(RMSE)of the training set and the test set are 0.037 41 and 0.055 16,and the determination coefficient R2 is 0.987 37 and 0.957 89,respectively.The research results can provide some references for predicting the development height of the water-con-ducting fracture zone in Binchang Coal Mine.关键词
导水裂隙带/煤矿智能化/随机森林算法/BP神经网络/矿井涌水Key words
water-conducting fracture zone/intelligent coal mine/random forest algorithm/BP neural network/mine water gushing分类
矿业与冶金引用本文复制引用
姬亚东,刘譞,朱开鹏,赵春虎,李凯,袁晨瀚,李盼盼,闫鹏珍..彬长矿区煤层采动导水裂隙带高度RF-BP模型预测对比研究[J].煤矿安全,2025,56(7):175-184,10.基金项目
中煤科工集团西安研究院有限公司科技创新基金资助项目(2021XAYKF02) （2021XAYKF02）
