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Forecast the Risk of Water Inrush from Coal Floor Based on Support Vector Machine Coupled with Component GIS

机译：基于支持向量机与部件GIS联合的支持向量机预测煤层涌出水中涌入的风险

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Aiming at the complexity and spatial variability of the water inrush from coal floor, the paper proposed a method of appraising the risk of water inrush based on coupled SVM with GIS. Firstly, using the new machine learning tool-support vector machine, the paper presents a new method of forecast of water inrush from coal floor based on least square support vector machine and constructs the prediction model. Overcoming the extra-learning problem of ANN, the complicated nonlinear relationship between the water inrush risk and its affected actors is presented as well. The SVM following the principle of structure risk minimization, the paper also analyzed how kernel parameter σ and penal factor C affect the forecast accuracy. Then the SVM-GIS coupled appraising model and the realizing method are introduced. The GIS realized the map layers management for complex spatial data efficiently. Finally a real case is studied and it is demonstrated that the method is feasible and has good potential application.
机译：针对的复杂性和从煤层底板突水的空间变异，本文提出评价突水的基于与GIS耦合SVM的风险的方法。首先，利用所述新的机器学习工具支持向量机，提出了突水预报的煤层底板基于最小二乘支持向量机的新方法和构建预测模型。克服ANN的额外的学习问题，突水风险及其影响参与者之间的复杂的非线性关系，提出为好。以下结构风险最小化原则的SVM，本文还分析了内核参数σ和惩罚因子C是如何影响预测的准确性。然后，SVM-GIS耦合评价模型和被引入的实现方法。所述GIS有效地实现了复杂的空间数据的地图层管理。最后一个真实的案例进行了研究，它表明，该方法是可行的，具有良好的应用前景。

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