煤田地质与勘探2024，Vol.52Issue(11)：72-85,14.DOI:10.12363/issn.1001-1986.24.03.0186
SMOGN过采样下导水裂隙带高度的MPSO-BP预测模型
A SMOGN-based MPSO-BP model to predict the height of a hydraulically conductive fracture zone
摘要
Abstract
[Objective]The height of a hydraulically conductive fracture zone,a significant factor influencing roof water inrushes and groundwater resource loss,is identified as a research focus of the prevention and control of mine water dis-asters.[Methods]To accurately predict the heights of hydraulically conductive fracture zones in coal seam roofs,five parameters were selected as the primary factors influencing hydraulically conductive fracture zones the mining depth:mining height,coal seam inclination,the length of the mining face along its dip direction,proportional coefficient of hard rocks(i.e.,the ratio of the cumulative thickness of hard rocks within the statistical height above the coal seam roof to the statistical height),and mining method.A total of 200 measured samples concerning the heights of hydraulically conductive fracture zones were collected as the model dataset.First,over-sampling of the original dataset was conduc-ted using the synthetic minority over-sampling technique for regression(SmoteR)combined with the introduction of Gaussian Noise(SMOGN).In conjunction with 8-fold cross-validation,the optimal back propagation(BP)neural net-work structure was determined by using the mean absolute error(denoted by EMA),root mean square error(denoted by ERMS),and coefficient of determination(denoted by R2)as the assessment indices of the regression model.Then,the ini-tial weights and thresholds of the BP neural network were optimized using the mutation particle swarm optimization(MPSO)algorithm.Finally,the optimized prediction model,i.e.,the MPSO-BP model,was applied to the engineering field.[Results and Conclusions]The results indicate that based on the original dataset,the BP neural network,using the Huber loss and Adam first-order optimization algorithm,enhanced the training speed and stability.Consequently,the op-timal activation function was determined at Tanh and the optimal hidden layer node number at 12.The MPSO-BP mod-el yielded the optimal performance where the MPSO population number was 50.After SMOGN and MPSO,the training set yielded an EMA value of 0.163,an ERMS value of 0.216,and an R2 value of 0.948,and these values were 0.260,0.341,and 0.901,respectively,for the validation set.The field application indicated that the MPSO-BP model yielded relative errors of below 9%in the prediction.Therefore,the integration of the SMOGN and MPSO can significantly enhance the stability and generalization capability of the prediction model,the sample distribution characteristics,the sample utiliza-tion efficiency,and the predicted effects of the model.This study can serve as a reference for the training and prediction of models for the heights of hydraulically conductive fracture zones.关键词
煤矿防治水/回归过采样/导水裂隙带/高度预测/变异粒子群算法/模型优化Key words
prevention and control of mine water hazard/over-sampling for regression/hydraulically conductive frac-ture zone/height prediction/mutation particle swarm optimization(MPSO)algorithm/model optimization分类
矿业与冶金引用本文复制引用
刘奇,梁智昊,訾建潇..SMOGN过采样下导水裂隙带高度的MPSO-BP预测模型[J].煤田地质与勘探,2024,52(11):72-85,14.基金项目
国家自然科学基金项目(51904168) （51904168）
山东省自然科学基金项目(ZR2023ME021) （ZR2023ME021）
青岛市博士后基金项目(QDBSH20230202050) （QDBSH20230202050）
