计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2014年
10期
233-237
,共5页
随机森林%因子分析%煤与瓦斯突出%强度预测
隨機森林%因子分析%煤與瓦斯突齣%彊度預測
수궤삼림%인자분석%매여와사돌출%강도예측
random forest%factor analysis%coal and gas outburst%strength prediction
煤与瓦斯突出是煤矿开采过程中主要的动力灾害之一,针对煤与瓦斯突出等级预测问题,提高突出预测的准确率,选取最大主应力、瓦斯压力、瓦斯含量、顶板岩性、距断裂距离、煤层厚度、开采垂深、绝对瓦斯涌出量和相对瓦斯涌出量9个影响因素作为煤与瓦斯突出等级预测的评价指标,同时对相关程度较高的评价指标进行因子分析,提取公共因子,用随机森林算法进行训练预测,建立了基于因子分析的煤与瓦斯突出预测的随机森林模型。通过煤矿实测19组煤与瓦斯突出的数据作为训练样本数据集进行模型的训练,5组数据作为该预测模型的测试数据,进行煤与瓦斯突出预测,同时通过其他预测模型预测结果的对比,验证了随机森林算法在煤与瓦斯突出预测中具有较高的准确度。
煤與瓦斯突齣是煤礦開採過程中主要的動力災害之一,針對煤與瓦斯突齣等級預測問題,提高突齣預測的準確率,選取最大主應力、瓦斯壓力、瓦斯含量、頂闆巖性、距斷裂距離、煤層厚度、開採垂深、絕對瓦斯湧齣量和相對瓦斯湧齣量9箇影響因素作為煤與瓦斯突齣等級預測的評價指標,同時對相關程度較高的評價指標進行因子分析,提取公共因子,用隨機森林算法進行訓練預測,建立瞭基于因子分析的煤與瓦斯突齣預測的隨機森林模型。通過煤礦實測19組煤與瓦斯突齣的數據作為訓練樣本數據集進行模型的訓練,5組數據作為該預測模型的測試數據,進行煤與瓦斯突齣預測,同時通過其他預測模型預測結果的對比,驗證瞭隨機森林算法在煤與瓦斯突齣預測中具有較高的準確度。
매여와사돌출시매광개채과정중주요적동력재해지일,침대매여와사돌출등급예측문제,제고돌출예측적준학솔,선취최대주응력、와사압력、와사함량、정판암성、거단렬거리、매층후도、개채수심、절대와사용출량화상대와사용출량9개영향인소작위매여와사돌출등급예측적평개지표,동시대상관정도교고적평개지표진행인자분석,제취공공인자,용수궤삼림산법진행훈련예측,건립료기우인자분석적매여와사돌출예측적수궤삼림모형。통과매광실측19조매여와사돌출적수거작위훈련양본수거집진행모형적훈련,5조수거작위해예측모형적측시수거,진행매여와사돌출예측,동시통과기타예측모형예측결과적대비,험증료수궤삼림산법재매여와사돌출예측중구유교고적준학도。
Coal and gas outburst is one of the principal dynamic disasters in coal mine underground mining. Aimed at solving the prediction problem of coal and gas outburst risk level, improve the outburst prediction accuracy, this paper selects 9 factors as evaluation index which affects the coal and gas outburst classification prediction, including maximum principal stress, gas pressure, gas content, roof lithology, distance from the fracture of roof, coal seam thickness, mining depth, the absolute gas emission and relative gas emission, and do the factor analysis to the most related evaluation index, extract the public factor, with random forest algorithm training, establish the coal and gas outburst prediction model based on factor analysis and random forest. Based on 19 groups of coal and gas outburst data measured by coal mine as the training sample data sets for model training, 5 groups of data as test data for model testing, do the prediction of coal and gas outburst, meanwhile compared with other prediction models, the test results verify the random forest algorithm in coal and gas out-burst prediction has higher accuracy.