Forecast of inrushed water volume grade from coal floor based on support vector machine with particle swarm optimization
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摘要: 为更好地解决支持向量机(SVM)核参数和惩罚因子的取值对煤层底板突水量等级预测精度的影响问题,提出利用全局搜索能力较强的粒子群优化(PSO)算法优化支持向量机参数。选取含水层水压、隔水层厚度、岩溶发育程度、断层规模等作为影响煤层底板突水量等级的因素,利用华北聚煤区煤层底板突水的实测数据进行训练,建立了煤层底板突水量等级预测的粒子群-支持向量机(PSO-SVM)模型,并将其应用于其他样本的预测。应用表明:模型能够较好地解决煤层底板突水量等级预测中存在的小样本、非线性等问题,预测结果与实际情况吻合程度高,具有较强的实用性和有效性。Abstract: To solve the problem of penalty factor and kernel parameter of support vector machine (SVM) which will affect the forecast accuracy, the method was put forward to find the better parameter value by using particle swarm optimization (PSO) which can automatically search the parameters for SVM. Four indexes, including water pressure, the thickness of aquifuge, karst development degree, the fault scale, were selected as the factors influencing water inrush from coal floor, the actual cases of water inrush from coal floor in Northern China coalfield were taken as training samples, the PSO-SVM model for forecast of water inrush volume grade from coal floor was established and applied to test other cases. The application of the model indicated that the method can solve the small sample, nonlinear problem, and the results obtained is better in accordance with the practice. It is practical and effective in forecasting water inrush volume grade from coal floor.
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期刊类型引用(2)
1. 关志阳. 基于双目视觉的机器人钻杆抓取系统研究. 煤矿机械. 2025(02): 222-224 . 
百度学术
2. 王清峰，刘洋，陈航，史书翰，崔小超. 煤矿井下钻进工况参数智能控制技术发展与展望. 矿业安全与环保. 2025(01): 20-29 . 百度学术
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