基于條件擴(kuò)散模型的未測(cè)量流域徑流預(yù)測(cè)方法

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中圖分類號(hào)：TP391.4；TP183 文獻(xiàn)標(biāo)識(shí)碼：A 文章編號(hào)：2096-4706（2025）08-0071-07

Abstract： Deep Learning becomes apowerful tool for runoff prediction，but in ungauged basins，the lack of flow observation data makes model trainingand prediction usuallyrequiretheapproachof Transfer Learning.However，thetarget basinoftendoesnothaveenoughdataforfie-tuning，whichmakes itdiffculttocalibratethemodelparameters.Therefore， this paper proposes anungauged basins runof prediction methodbasedonconditional diffusion model.The method includesa forwardnoisingprocessandareversedenoisingprocessThedenoisingmodelis trainedinthesourebasinandthenthedatais recoveredfromthenoiseintetargetbasinasthepredictionresultInadition，thedenoisingprocesisguidedbytheconditional datancludingmeteorologicaldriversandhistoricalrunoffndtheTrasformerlayerisintroducedintothedenoisingmodelto capture the dependenceof ime andfeatures.Throughthecross-validation experimentontheCAMELS-US dataset，theresults show that the method has superiority.

Keywords： runoff prediction; ungauged basins; Transfer Learning; conditional diffusion model; CAMELS-US

0 引言

徑流量能夠反映特定流域內(nèi)水文、王壤和地質(zhì)特征，是綜合反映流域內(nèi)自然條件和人類活動(dòng)的重要指標(biāo)。（剩余7609字）