基于条件扩散模型的未测量流域径流预测方法  

Ungauged Basin Runoff Prediction Method Based on Conditional Diffusion Model

作　　者：张文昭 严华[1] ZHANG Wenzhao;YAN Hua(College of Electronics and Information Engineering,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学电子信息学院,四川成都610065

出　　处：《现代信息科技》2025年第8期71-76,82,共7页Modern Information Technology

摘　　要：深度学习已成为径流预测的强大工具,但在未测量流域中,流量观测数据的缺乏使得模型训练和预测通常需要采用迁移学习的方式。然而,目标流域往往没有足够的数据用于微调,导致模型参数难以校准。因此,文章提出一种基于条件扩散模型的未测量流域径流预测方法。该方法包含一个正向加噪过程和一个反向去噪过程。在源流域中训练去噪模型,然后在目标流域上从噪声中恢复数据作为预测结果。此外,通过包含气象驱动和历史径流的条件数据来指导去噪过程,并在去噪模型中引入Transformer层,以捕捉时间和特征的依赖性。通过在CAMELS-US数据集上进行交叉验证实验,结果表明该方法具有优越性。Deep Learning becomes a powerful tool for runoff prediction,but in ungauged basins,the lack of flow observation data makes model training and prediction usually require the approach of Transfer Learning.However,the target basin often does not have enough data forfine-tuning,which makes it difficult to calibrate the model parameters.Therefore,this paper proposes an ungauged basins runoffprediction method based on conditional diffusion model.The method includes a forward noising process and a reverse denoising process.The denoising model is trained in the source basin,and then the data is recovered from the noise in the target basin as the prediction result.In addition,the denoising process is guided by the conditional data including meteorological drivers and historical runoff,and the Transformer layer is introduced into the denoising model to capture the dependence of time and features.Through the cross-validation experiment on the CAMELS-US dataset,the results show that the method has superiority.

关 键 词：径流预测 未测量流域 迁移学习 条件扩散模型 CAMELS-US 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]