基于核主成分分析和增强极限学习机方法的煤与瓦斯突出危险性预测

Kailong Xue , Yun Qi , Hongfei Duan , Anye Cao , Aiwen Wang
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摘要

为了提高煤与瓦斯突出预测的准确性和效率,提出了一种将核主成分分析(KPCA)与改进的鲸鱼优化算法(IWOA)优化的极限学习机(ELM)相结合的新方法,用于矿井煤与瓦斯突出灾害的精确预测。首先,根据煤与瓦斯突出灾害的影响因素,选取瓦斯压力、地质构造、瓦斯涌出初速、煤层构造类型等9个耦合指标;利用SPSS 27中的Pearson相关系数矩阵分析各指标之间的相关性,然后通过核主成分分析(Kernel principal Component Analysis, KPCA)提取原始数据的主成分。通过引入自适应权值、可变螺旋位置更新和最优邻域扰动对Whale优化算法(WOA)进行了改进。随后采用改进的Whale Optimization Algorithm (IWOA)对极限学习机(Extreme Learning Machine, ELM)输入层的权值(weight)和隐藏层的阈值(threshold) g进行优化,从而提高了极限学习机(Extreme Learning Machine, ELM)的预测精度,并在一定程度上缓解了ELM相关的“过拟合”问题。利用KPCA提取的主成分作为输入,突出危险性等级作为输出。随后,将这些结果与WOA-SVC、PSO-BPNN和SSA-RF模型的结果进行了比较分析。IWOA-ELM模型准确地预测了煤与瓦斯突出灾害的风险等级,结果与实际情况相符。与其他测试模型相比,该模型的性能表现为Ac分别提高0.2、0.3和0.2;P值分别增加0.15、0.2167、0.1333;R分别增加0.25、0.3、0.2333;F1-Score分别增加0.2031、0.2607、0.1864;Kappa系数k分别增加0.3226、0.4762和0.3175。通过对山西某煤矿的实际应用,验证了IWOA-ELM模型的实用性和稳定性,预测值与实际值吻合较好。这表明该模型更适合于煤与瓦斯突出灾害风险的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of coal and gas outburst hazard using kernel principal component analysis and an enhanced extreme learning machine approach
In order to enhance the accuracy and efficiency of coal and gas outburst prediction, a novel approach combining Kernel Principal Component Analysis (KPCA) with an Improved Whale Optimization Algorithm (IWOA) optimized extreme learning machine (ELM) is proposed for precise forecasting of coal and gas outburst disasters in mines. Firstly, based on the influencing factors of coal and gas outburst disasters, nine coupling indexes are selected, including gas pressure, geological structure, initial velocity of gas emission, and coal structure type. The correlation between each index was analyzed using the Pearson correlation coefficient matrix in SPSS 27, followed by extraction of the principal components of the original data through Kernel Principal Component Analysis (KPCA). The Whale Optimization Algorithm (WOA) was enhanced by incorporating adaptive weight, variable helix position update, and optimal neighborhood disturbance to augment its performance. The improved Whale Optimization Algorithm (IWOA) is subsequently employed to optimize the weight ф of the Extreme Learning Machine (ELM) input layer and the threshold g of the hidden layer, thereby enhancing its predictive accuracy and mitigating the issue of "over-fitting" associated with ELM to some extent. The principal components extracted by KPCA were utilized as input, while the outburst risk grade served as output. Subsequently, a comparative analysis was conducted between these results and those obtained from WOA-SVC, PSO-BPNN, and SSA-RF models. The IWOA-ELM model accurately predicts the risk grade of coal and gas outburst disasters, with results consistent with actual situations. Compared to other models tested, the model's performance showed an increase in Ac by 0.2, 0.3, and 0.2 respectively; P increased by 0.15, 0.2167, and 0.1333 respectively; R increased by 0.25, 0.3, and 0.2333 respectively; F1-Score increased by 0.2031, 0.2607, and 0.1864 respectively; Kappa coefficient k increased by 0.3226, 0.4762 and 0.3175, respectively. The practicality and stability of the IWOA-ELM model were verified through its application in a coal mine in Shanxi Province where the predicted values exactly matched the actual values. This indicates that this model is more suitable for predicting coal and gas outburst disaster risks.
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