摘要
Abstract
Quickly identifying the type of water inrush source is a key part of mine water damage prevention and control.To realize the accurate identification of mine water sources from the Pingdingshan Coalfield,water samples from different aqui-fers,such as surface water,Quaternary pore water,Carboniferous tuff karst water,Permian sandstone water,and Cambrian tuff karst water,were extracted,respectively,and the key discriminatory indexes,Na++K+,Ca2+,Mg2+,Cl-,SO42-,and HCO3-,were selected for the analysis.To avoid model overfitting due to the interference of outlier data,the paper utilizes box plots to show the discrete distributions of the data accurately,and twenty sets of outliers are quickly identified from the data to clean the study data.The cleaned data is divided into learning and test samples in the ratio of 8∶2,and the learning sam-ples are fed into the Light Gradient Boosting Machine(LightGBM)for model training.The tree-structured Parson estimator(TPE)is used to optimize the main parameters of LightGBM and construct the TPE-LightGBM model.Comparing the results of LightGBM with those of TPE-LightGBM,the model's accuracy is improved by 13.9％,which indicates that the TPE algorithm is effective.To further vali-date the performance of the model,the experimental results are compared with the Random Search-Multi-Layer Perceptron Machine(RS-MLP)and Genetic Algorithm-Extreme Gradient Boosting Tree(GA-XGBoost)models.The results show that the TPE-LightGBM model has higher accuracy and lower generalization error,which indicates that TPE-LightGBM is more ad-vantageous and applicable in water source identification.The contribution of the variables was quantified using the Gini coef-ficients,and based on the calculations,it is clear that Ca2+has the highest contribution,so it is necessary to pay attention to the changes in the concentration of Ca2+.关键词
平顶山煤田/矿井水害/树状结构帕森估计器(TPE)/光梯度提升机(LightGBM)/智能化水源辨识模型Key words
Pingdingshan Coalfield/mine water hazard/tree-structured Parson estimator(TPE)/light gradient boosting ma-chine(LightGBM)/intelligent water source identification model分类
矿山工程
