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1、摘要瓦斯是危害矿井安全生产的重要因素之一。瓦斯灾害是煤矿中最严重的灾害之一。采场范围内涌出瓦斯的地点称为瓦斯源,瓦斯涌出源的多少,各源涌出瓦斯量的大小直接影响着采场的瓦斯涌出量。瓦斯灾害治理的好坏已成为矿井特别是高瓦斯矿井兴衰存亡的关键因素之一。近年来，随着煤炭开采在规模和产量上的逐年扩大，煤矿安全生产成为国内外面临的重大课题。瓦斯含量预测取决于多因素、非线性的函数关系的建立,预测模型建立的准确与否决定于各个影响因素之间的相互作用、相互耦合的特性。本文通过深入研究数据挖掘技术及其挖掘算法，建立瓦斯涌出量评估模型，并深入研究了关联规则数据挖掘的Apri
2、ori算法，提出改进。利用改进的Apriori算法对在瓦斯涌出量构成因素的数据进行挖掘，寻找有价值的信息，其中包含有38个典型样本，并且将检验结果分别与回归模型、标准BP神经网络、自适应BP神经网络的预测结果进行比较。结果表明：遗传神经网络模型可靠,预测精度高,为促进软计算技术与瓦斯地质的结合奠定了基础。为预测瓦斯含量，保障煤矿安全提供有价值的参考。关键词：瓦斯溢出量，数据挖掘，关联规则，Apriori算法IABSTRACTThegasisoneofthemostimportantfactorswhichendangerthesecurityofthe
3、miningproduction.Thegashazardisoneofthemostserioushazards.Thelocaloftherangeoftheminingexploitfromwhichthegasemitisnamedtheoriginofthegas.Thenumberoftheoriginofthegasemissionandtheamountofthedifferentoriginofthegasemissionaffecttheminingexploit'sgasemissionquantitydirectly.Inrec
4、entyears,withtheoutputofcoalminingonthesizeandhasexpanded,coalminesafetyproductionhasbecomeamajorissuefacing。Forecastingthegascontentdependsonanestablishmentofanonlinerfunctionalrelationofmanyfactors;theaccuracyoftheforecastingmodelforthegascontentisdeterminedbythepeculiaritieso
5、ftheinteractionandcouplingbetweenalltheaffectingfactors.Thispaperbuildsevaluationmodelofgasemissionbymeansofdeeplystudyingdataminingtechnologyandalgorithm,deeplystudiesApriorialgorithmofassociationrulesdataminingandputsforwardimproving.TheimprovedApriorialgorithmisusedtominethec
6、omponentsdataofgasemissioninordertofindvaluablemessage,whichcanprovidevaluablereferencesforforecastinggascontentandProtectingCoalMineSafety.Finallybysimulationverification,Onthebasisofthedatainlaboratory,thetrainingandtestingsamplesofimprovedApriorimodelarefounded,including38typ
7、icalspecimens.Theverifyingutcomehasbeencomparedwiththeoutputofbackassaymodel,normalBPNN,andautoadaptingNN.TheresultshowsthattheimprovedApriorimodelisreliableandprecise,whichfoundsthebasisforpromotingtheintegrationofsoftcalculationandgasgeology.thismodelplaysanindelibleroleineval
8、uationofgasemission.Keywords:gasemission,datami
