Ling Jierui, Fu Zhibo, Xue Kailong
School of Coal Engineering, Shanxi Datong University Datong 037000, China.
Heliyon. 2024 Aug 3;10(15):e35708. doi: 10.1016/j.heliyon.2024.e35708. eCollection 2024 Aug 15.
Mine water inrush accident is one of the most threatening disasters in coal mine production process. In order to improve the identification accuracy of mine water inrush source, a fast identification method of mine water inrush source based on improved sparrow search (SSA) algorithm coupled with Random Forest algorithm was proposed. Firstly, taking Zhaogezhuang Mine as the research object, six factors were selected as the discriminant index and three principal components were extracted by kernel principal component analysis. Secondly, four strategies are employed to enhance the SSA for achieving the ISSA, while multiple benchmark functions are utilized to validate its performance. The extracted principal components serve as input, and the categories of water inrush sources act as output. Subsequently, the prediction results of Random Forest (RF) algorithm after optimizing hyperparameters through Improve SSA are compared with those obtained from other models. The research findings demonstrate that optimizing the RF model using Improve SSA yields superior predictive performance compared to alternative models. Finally, this model is applied to identify water inrush sources in a mine located in Shandong province. The discrimination results exhibit higher accuracy, precision, recall, and F1 index than other models, thereby confirming the reliability and stability of this approach. The results demonstrate that the kernel principal component analysis-based rapid identification model for mine water outburst source, combined with an improved sparrow search algorithm to optimize Random Forest, exhibits excellent robustness and accuracy. This model effectively fulfills the requirements of identifying mine water outbursts and provides a reliable guarantee for ensuring mining safety production.
矿井突水事故是煤矿生产过程中最具威胁性的灾害之一。为提高矿井突水水源的识别精度，提出了一种基于改进麻雀搜索（SSA）算法与随机森林算法相结合的矿井突水水源快速识别方法。首先，以赵各庄矿为研究对象，选取6个因素作为判别指标，通过核主成分分析提取3个主成分。其次，采用4种策略对SSA进行改进以实现ISSA，同时利用多个基准函数验证其性能。将提取的主成分作为输入，突水水源类别作为输出。随后，将通过改进SSA优化超参数后的随机森林（RF）算法预测结果与其他模型的预测结果进行比较。研究结果表明，与其他模型相比，使用改进SSA优化RF模型具有更好的预测性能。最后，将该模型应用于山东省某矿井突水水源的识别。判别结果在准确率、精确率、召回率和F1指数方面均高于其他模型，从而证实了该方法的可靠性和稳定性。结果表明，基于核主成分分析的矿井突水水源快速识别模型，结合改进的麻雀搜索算法优化随机森林，具有优异的鲁棒性和准确性。该模型有效地满足了识别矿井突水的要求，为保障煤矿安全生产提供了可靠保证。
