基于机器学习的煤矿突水预测方法

由于影响煤矿突水的因素多、相关性强,影响模型预测精度;数据收集工作量大,成本较高,如何科学地选取特征以提高模型预测准确率成为本文重点研究内容.本文首先提出采用稳定性选择方法在已知的22个影响因素中选取7个最重要的因素,之后构建随机森林、神经网络以及支持向量机3种典型机器学习分类预测模型对特征选取前后的数据进行预测分析,实验结果表明,特征选取后的预测模型非常稳定且预测准确率可达100%,同时降低了样本数据收集成本.

Prediction Method of Coal Mine Water Inrush Based on Machine Learning

Because there are many factors affecting coal mine water inrush and they have strong correlation, the prediction accuracy of the model will be affected. Due to the heavy workload and high cost of data collection, how to select features scientifically to improve the accuracy of model prediction has become the focus of this study. At first, this study uses stability selection to select 7 factors which are more important in 22 known influence factors, and then builds three typical machine learning classification forecasting models including random forest, neural network, and support vector machine (SVM) to forecast the data before and after feature selection. The experimental results show that the prediction model is very stable after the feature selection and prediction accuracy can reach 100%, and also decrease the cost of the sample data collection.