Evaluation of deep coal and gas outburst based on RS-GA-BP
Evaluation of deep coal and gas outburst based on RS-GA-BP
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DOI：
10.1007/s11069-022-05652-w
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发表时间：
2022-10
期刊：
影响因子：
3.7
通讯作者：
Junqi Zhu;Haotian Zheng;Li Yang;Shanshan Li;Liyan Sun;Jichao Geng
中科院分区：
文献类型：
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作者：
Junqi Zhu;Haotian Zheng;Li Yang;Shanshan Li;Liyan Sun;Jichao Geng
针对深部煤矿瓦斯突出的高维和非线性特点，提出一种系统筛选和整合深部煤矿瓦斯数据的智能评价方法，以有效识别深部煤矿与瓦斯突出。引入遗传算法改进的粗糙集对深部煤矿瓦斯复杂数据进行降维，确定深部煤与瓦斯突出的主控指标。随后，结合遗传算法的并行性和鲁棒性特点，对反向传播（BP）神经网络的初始权重和阈值进行优化。为合理识别深部煤矿瓦斯突出，建立了基于遗传算法反向传播的自适应优化BP神经网络（GA-BP）模型。数据拟合表明，与标准BP神经网络相比，该方法在提高灾害识别速度的同时，能够显着提高深层煤与瓦斯突出的检测精度，提高灾害识别的效率，从而将深层煤与瓦斯突出的风险识别准确率提高到90%。这不仅为深部煤与瓦斯突出风险的科学评价提供了新方法，也为其他高维非线性领域的科学评价提供了重要参考。
Owing to the high dimension and nonlinear characteristics of gas outbursts in deep coal mines, an intelligent evaluation method for systematically screening and integrating gas data in deep coal mines is proposed herein to effectively identify coal and gas outbursts in deep mines. A rough set improved using a genetic algorithm is introduced to reduce the dimension of complex data pertaining to deep coal mine gas to determine the main control index of deep coal and gas outbursts. Subsequently, the initial weight and threshold of a back propagation (BP) neural network are optimized by combining the characteristics of parallelism and robustness of the genetic algorithm. An adaptive optimization of BP neural network by genetic algorithm back propagation (GA-BP) model is established to identify gas outburst in deep coal mine reasonably. Compared with the standard BP neural network, data fitting shows that the method can significantly improve the detection accuracy of deep coal and gas outburst while improving the speed of disaster identification, as well as improve the efficiency of disaster identification, thereby increasing the risk identification accuracy of deep coal and gas outburst to 90%. This not only provides a new method for the scientific evaluation of deep coal and gas outburst risk, but also an important reference for the scientific evaluation of other high-dimensional and nonlinear fields.