基于随机森林算法的矿井涌水量预测

Mine Water Inflow Forecasting Based on Random Forest Algorithm

为对矿井涌水量进行准确预测,结合矿山地下水特征,分别选取采空区累积面积、降雨量、充水通道3个影响因素作为研究对象,利用20组矿井涌水量数据作为随机森林预测模型的训练数据集,进行模型的学习训练,另用3组边矿井涌水量数据作为预测模型的测试数据,通过训练好的矿井涌水量预测模型进行测试。

For the mine water iifflow of accurate prediction, combined with the groundwater characteristics of the mines, gob area accumulation area, rainfall, water filling channel three influencing faetorswere selected as the research object, using 20 groups of mine inflow datar as random forest prediction model for the training data set into the model for learning and training, and the other with three groups side of mine water inflow data as test data prediction model, through training of mine Chung content prediction model is tested.