基于PCA_Fuzzy_PSO_SVC的底板突水危险性评价

发布时间：2018-06-20 04:06

本文选题：底板突水 + 危险性评价　；参考：《煤炭学报》2015年01期

【摘要】：为解决煤层底板突水预测难题,提出了基于主成分分析、模糊数学、粒子群算法以及支持向量机分类的底板突水危险性评价模型,模型以支持向量机分类为基础,通过主成分分析将多种影响底板突水的因子归纳为构造主成分、水文地质主成分、煤层信息主成分及开采条件主成分,其中构造主成分及水文地质主成分为影响底板是否突水的最主要控制因素,模糊化主成分因子,利用粒子群算法优化支持向量机分类参数,根据已有数据资料建立了评价模型,并将该模型应用于实际中,得到了准确的预测结果,为底板突水危险性评价提供了一种新的方法。
[Abstract]:In order to solve the problem of water inrush prediction in coal seam floor, a risk assessment model based on principal component analysis (PCA), fuzzy mathematics, particle swarm optimization (PSO) and support vector machine (SVM) classification was proposed. Through principal component analysis, the factors affecting water inrush of floor are classified as structural principal component, hydrogeological principal component, coal seam information principal component and mining condition principal component. Among them, the structural principal component and hydrogeological principal component are the most important controlling factors that affect the water inrush of the bottom plate. The fuzzy principal component factor is used to optimize the classification parameters of the support vector machine (SVM), and the evaluation model is established according to the existing data. The model is applied to practice, and accurate prediction results are obtained, which provides a new method for evaluating the risk of water inrush on the floor.
【作者单位】：山东科技大学山东省沉积成矿作用与沉积矿产重点实验室;山东科技大学地球科学与工程学院;
【基金】：教育部高等学校博士学科点专项科研基金资助项目(20133718110004) 山东省自然科学基金重点资助项目(ZR2011EEZ002) 山东科技大学科研创新团队支持计划资助项目(2012KYTD101)
【分类号】：TD745.2

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