基于 KPCA-PSO-RF 矿井突水水源判别模型.
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| Title: | 基于 KPCA-PSO-RF 矿井突水水源判别模型. |
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| Alternate Title: | Discrimination of mine water inrush source based on KPCA-PSO-RF model. |
| Authors: | 刘伟韬1,2,3 skdlwt@126.com, 卢润时3, 杜衍辉1,2 dyhsdust@126.com |
| Source: | Coal Science & Technology (0253-2336). May2026, Vol. 54 Issue 5, p378-392. 15p. |
| Subject Terms: | *Mine water, *Random forest algorithms, *Optimization algorithms, *Particle swarm optimization, *Machine learning, *Composition of water, *Dimensional reduction algorithms |
| Abstract (English): | Under complex geological conditions, it is difficult to accurately determine the source of mine water samples by relying only on the traditional hydrochemical feature similarity discrimination method. Accordingly, by integrating machine learning algorithms with optimization algorithms, a novel discrimination model for mine water inrush sources based on the KPCA-PSO-RF framework is proposed. Firstly, based on the analysis of the hydrochemical characteristics of groundwater in the main aquifers, 11 hydrochemical indexes (K+ +Na+, Ca2+, Mg2+, Cl-,SO42-, HCO3-,CO32-, TDS, TH, TA, pH) are selected as the characteristic parameters of water source discrimination on the basis of analyzing the hydrochemical characteristics of groundwater in the main aquifers. Three main indexes are extracted by kernel principal component analysis (KPCA) as the discriminant factors of model identification. Then, the particle swarm optimization (PSO) algorithm is used to iteratively optimize the hyperparameters of random forest algorithm (RF). Finally, 96 groups of groundwater sample data are divided into training samples and test samples according to 7∶3 for training. The KPCA-PSO-RF model is established, and the discriminant results are compared with the RF, PSO-RF and KPCA-GridSearchCV-RF models. The results indicate that: After 5- fold cross validation, the Min-Max normalization method and KPCA algorithm are used to reduce the dimension of hydrochemical data and extract the first three principal components, which can effectively eliminate the redundancy and overlap between samples and make up for the limitation that traditional PCA algorithm cannot deal with complex nonlinear samples; PSO is used to iteratively optimize RF hyperparameters, and the optimal combination ( n_estimators = 54, max_depth = 11, min_samples_split = 7) is determined after 103 iterations, which can enhance the accuracy of model classification and eliminate the blindness of parameter setting; Compared with the relevant discriminant models, the KPCA-PSO-RF model is superior to the comparative model in terms of accuracy (96.55%), accuracy (97.32%), recall (98.21%), and F1 score (0.965 9). The generalization is better and the accuracy is higher. The data of 10 groups of mine water samples in Yangcheng Coal Mine were input into the trained model, and the discriminant results were consistent with the measured results of water inrush on site. It was accurately judged that the water inrush source of 1308 working face was Ordovician limestone aquifer, and the water inrush source of 3305 working face was three limestone aquifer, which realized accurate discrimination. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): | 在复杂地质条件下仅依赖传统水化学特征相似性判别方法难以精准判定矿井水水样来源, 因 此将机器学习算法与寻优算法相结合, 提出了一种基于 KPCA-PSO-RF 矿井突水水源判别模型。首 先在分析主要含水层地下水水化学特征的基础上选取 11 种水化学指标 (K ++Na+、Ca2+、Mg2+、Cl-、SO42-、HCO3-、CO32-、TDS、TH、TA、pH) 作为水源判别特征参数, 利用核主成分分析 (KPCA) 提 取 3 种主要指标作为模型判别的因子, 然后通过粒子群优化算法 (PSO) 对随机森林算法 (RF) 超参数 开展迭代寻优工作, 最后将 96 组地下水样本数据按 7∶3 划分为训练样本与测试样本进行训练, 建 立 KPCA-PSO-RF 模型, 并将判别结果与 RF、PSO-RF 和 KPCA-GridSearchCV-RF 模型进行对比。 结果表明: 经 5 折交叉验证, 运用 Min-Max 标准化方法与 KPCA 算法降维处理水化学数据并提取前 3 种主成分, 可以有效消除样本间的冗余与重叠, 并弥补传统 PCA 算法无法处理复杂非线性样本的 局限性; 运用 PSO 迭代寻优 RF 超参数, 经 65 次迭代确定最优组合 (n _estimators=54、max_depth=11、 min_samples_split=7), 可以增强模型分类准确性, 消除参数设置盲目性; 与相关判别模型比较, KPCA-PSO-RF 模型在准确率 (96.55% ) 、精确率 (97.32% ) 、召回率 (98.21% ) 、F1 分数 (0.965 9) 等方 面均优于对比模型, 其泛化性更佳, 准确性更高。将阳城煤矿 10 组矿井水水样数据输入到训练好的 模型中, 判别结果与现场突水实测结果一致, 准确判断 1308 工作面突水水源为奥灰含水层, 3305 工 作面涌水水源为三灰含水层, 实现了精准区分。 [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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