Comparative study on RF-BP model prediction of mining water-conducting fracture zone height in Binchang Coal Mine
JI Yadong
LIU Xuan
ZHU Kaipeng
ZHAO Chunhu
LI Kai
YUAN Chenhan
LI Panpan
YAN Pengzhen
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.
Keywords:water-conducting fracture zoneintelligent coal minerandom forest algorithmBP neural networkmine water gushing
Publication Date:2025-07-20
Online Publishing Date:2025-08-15(First online date of this platform, not the publication date of the document)
Pages:10( 175-184 )
