基于Word2Vec模型的泥石流多源災(zāi)害數(shù)據(jù)融合研究

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關(guān)鍵詞：泥石流災(zāi)害；知識抽?。毁|(zhì)量評估；知識融合；Word2Vec

中圖分類號：P694文獻標志碼：A doi：10.3969/j.issn.1000-1379.2025.07.016

引用格式：，，，等.基于Word2Vec模型的泥石流多源災(zāi)害數(shù)據(jù)融合研究[J].人民黃河，2025，47（7）：97-102.

Research on Multi-Source Debris Flow Disaster Data Fusion Based on Word2Vec Model

JIN Lei1，XU Peng2，LI Jie2，CAI Yingchun1，3，YANG Haibo1，3

（1.School of Water Conservancy and Transportation， Zhengzhou University， Zhengzhou 45ooo1，China; 2.Guiyang Engineering Corporation Limited，Power Construction Corporation of China，Guiyang 550081，China; 3.NationalKeyLaboratoryofTunnel Boring MachineandInteligentOperationand Maintenance，Zhengzhou450oo1，China） Abstract：Underkgdofdeelotoftateodtialteleles，s flowdisastersisilseitactesgiuouedessdaillo tionmodellibrasuchsjbdLteadectustucuredebrsfsastedatabseetlygegaigl intoadatabasetoachevedatafusionBymappingwodsintoaigdimesioalspacetrouhteWordVecodel，texualvocablaryaso vertedintoalvadtoprsentatiosndKeeCAereaplidtoucetimesioalitoftvctorsdthKmeansalgoitmwasusedforlusteringandvisualization.Theresultssowtatindataextractionevaluation，theveragevaluesofosistecy， completenesandacuacyareallabove8Oiheansuaredevaoselow5.CmparingthCAandtSNEdimnsioalitucto metodsusingeeScoSS）taatecseiscSSvue59pefogtd， whichhasanSSueof.3einmostateattCAdesperpefoancditioallhertdeliis strongontealuestadigsoiablefoebsoastedataetaceveragigthOieureftedecod eltoobtaind，taltftallaaoegeiggsdyuli sourcedebrisfasteatadaticsisteissdyduseptchataeactiosioalitutd clusteringuliatelyprovdingabeakouhhcalethoebrisdasterdataogsifatiodmanticocyfu sion，as well as an important technical solution for disaster data integration.

Key words： debris flow disaster;knowledge extraction；quality evaluation； knowledge fusion；Word2Vec

0 引言

受地理與氣候條件的綜合影響，我國泥石流災(zāi)害發(fā)生頻率較高，是世界上受該類災(zāi)害影響較為嚴重的國家之一[1-5]。（剩余9385字）