School of Earth and Environment, Anhui University of Science and Technology

为精准判别矿井突水水源，提出1种基于PCA-ISSA-BP模型的矿井涌水水源判别模型。选择孙疃煤矿不同含水层水的6种常规离子和pH值作为判别指标，首先使用主成分分析对7种指标进行降维处理；然后通过Sine混沌映射、融合正弦余弦算法和Lévy飞行策略改进SSA对BP模型进行参数寻优；最后在Matlab软件上编程实现模型的建立。结果表明：主成分分析降低了指标之间的相关性，提高了模型的判别精度，经过归一化处理和参数优化的BP神经网络模型能够跳出局部极小值点，且收敛速度有了较大的提升；与ISSA-BP、PCA-SSA-BP、PCA-PSO-BP和PCA-BP模型相比，PCA-ISSA-BP模型具有更高的识别精度、更强的拟合能力以及更小的均方误差，能够更精准实现矿井突水水源的识别。

In order to accurately identify the source of mine water inrush, a discriminant model of mine water inrush source based on PCA-ISSA-BP model is proposed. Six conventional ions and PH of different aquifer water in Suntuan Coal Mine were selected as discriminant indexes. Firstly, principal component analysis was used to reduce the dimension of seven indexes. Then, Sine chaotic mapping, fusion sine cosine algorithm and Lévy flight strategy were used to improve SSA to optimize the parameters of BP model. Finally, the model was established by programming on Matlab software. The results show that the principal component analysis reduces the correlation between the indicators and improves the discriminant accuracy of the model. The BP neural network model after normalization and parameter optimization can jump out of the local minimum point, and the convergence speed has been greatly improved. Compared with the ISSA-BP, PCA-SSA-BP, PCA-PSO-BP and PCA-BP models, the PCA-ISSA-BP model has higher recognition accuracy, stronger fitting ability and smaller mean square error. The model can more accurately identify the source of mine water inrush.

刘梦琪，王来斌，林征. 基于PCA-ISSA-BP模型的矿井突水水源判别[J]. 煤矿安全，2023，54（11）：179−187.

LIU Mengqi, WANG Laibin, LIN Zheng. Identification of mine water inrush source based on PCA-ISSA-BP model[J]. Safety in Coal Mines, 2023, 54（11）: 179−187.