基于KPCA-GWO-SVM的矿井突水水源识别

华星月; 邵良杉

基于KPCA-GWO-SVM的矿井突水水源识别

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出版历程
- 发布日期: 2023-02-19

Mine water inrush source identification model based on KPCA-GWO-SVM

摘要

摘要: 为提高矿井突水水源识别的精准度，提出1种基于KPCA-GWO-SVM的矿井突水水源识别模型；该算法利用核主成分分析（KPCA）进行特征降维，加快水源识别速度，通过灰狼优化算法（GWO）搜寻支持向量机（SVM）的最优参数，使水源识别精准度更高；以赵各庄矿为研究对象，分析各含水层主要水化学类型，选取6种离子指标，经KPCA提取3个主成分，随机选取总样本量70%为训练集（共47组），30%作为预测集（共20组），构建KPCA-GWO-SVM模型并与KPCA-PSO-SVM、KPCA-WOA-SVM和KPCA-SVM模型对比。结果表明：KPCA-GWO-SVM的水源预测结果与实际结果一致，比未经KPCA处理模型的预测准确率高10%且寻优速度更快；与其他模型相比准确率最高，具有优越性。
- 矿井突水 /
- 水源识别 /
- 核主成分分析（KPCA） /
- 灰狼优化算法（GWO） /
- 支持向量机（SVM）
Abstract: In order to improve the accuracy of mine water inrush source identification, a KPCA-GWO-SVM-based mine water inrush source identification model is proposed. The algorithm uses kernel principal component analysis（KPCA） for feature dimension reduction to speed up water source identification, and searches for the optimal parameters of support vector machine（SVM） through graywolf optimization（GWO） algorithm to make water source identification more accurate. Taking Zhaogezhuang Mine as the research object, analyzing the main hydro-chemical types of each aquifer, selecting 6 ion indicators, extracting 3 principal components by KPCA, and randomly selecting 70% of the total sample size as the training set （47 groups in total）, 30% as a prediction set（20 groups in total）, the KPCA-GWO-SVM model was constructed and compared with the KPCA-PSO-SVM, KPCA-WOA-SVM and KPCA-SVM models. The results show that the water source prediction results of KPCA-GWO-SVM are consistent with the actual results, which is 10% higher than the prediction accuracy of the model without KPCA processing, and the optimization speed is faster. Compared with other models, the model proposed in this paper has the highest accuracy rate and has superiority.
- mine water inrush /
- water source identification /
- kernel principal component analysis（KPCA） /
- graywolf optimization algorithm（GWO） /
- support vector machine（SVM）

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参考文献(15)

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