S-UNet:基于U-Net和LSTM的短临降水预报网络  

S-UNet:Short-term Precipitation Forecasting Network Based on U-Net and LSTM

作　　者：许梦 杜景林[2] 刘蕊 XU Meng;DU Jing-Lin;LIU Rui(School of Computer Science&School of Cyber Science and Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,China;School of Artificial Intelligence(School of Future Technology),Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学计算机学院、网络空间安全学院,南京210044 [2]南京信息工程大学人工智能学院(未来技术学院),南京210044

出　　处：《计算机系统应用》2024年第11期48-57,共10页Computer Systems & Applications

摘　　要：随着深度学习技术的发展,多数研究工作将短临降水预报视为雷达回波序列的预测任务.由于降水复杂性的非线性时空变换,现有的短临预报存在准确性低、外推时效短、难以应对复杂的非线性时空变换等缺点.为解决以上问题,本文基于U-Net和LSTM提出了S-UNet短临降水预报网络.首先本文提出了S-UNet layer (SL)模块,以帮助网络更好地提取雷达序列特征,构建时空变化的整体趋势,从而提高网络效率,增加网络的外推时长.其次,为更好地应对雷达回波的变形、积累和消散的复杂性,增强网络对复杂的空间关系的捕获能力和运动轨迹的模拟能力,本文基于LSTM构建了雷达特征模块radar feature (RF).最后,将SL模块和RF模块与U-Net框架结合,提出了S-UNet短临降水预报网络,并在KNMI数据集上实现了先进的性能.实验结果表明,在KNMI的NL-50和NL-20数据集上,本文所提的方法与主流方法相比,海德克技能得分和关键成功指数分别提高了5.25%(6.57%)和2.17%(4.75%),达到了0.30 (0.29)和0.72 (0.58);准确率提高了2.10%(1.35%),达到了0.80 (0.80);假接受率降低了4.27%(1.80%),达到了0.24 (0.38).除此之外,本文通过消融实验证明了所提出模块及结合方法的有效性.The development of deep learning technology invites most research to consider short-term precipitation nowcasting as a prediction task of radar echo sequences.Due to the nonlinear spatiotemporal transformations involved in the complexity of precipitation,existing short-term nowcasting methods have problems such as low accuracy,short extrapolation time,and difficulty in dealing with complex nonlinear spatiotemporal transformations.To address these issues,this study proposes an S-UNet short-term precipitation forecasting network based on U-Net and LSTM.Firstly,the study introduces the S-UNet layer(SL)module to help the network better extract radar sequence features and construct the overall trend of spatiotemporal changes,thereby improving the network efficiency and increasing the extrapolation duration.Secondly,to better address the complexity of radar echo deformation,accumulation,and dissipation,and to enhance the network’s ability to capture complex spatial relationships and simulate movement trajectories,this study constructs the radar feature(RF)module based on LSTM.Finally,by combining the SL module and the RF module with the U-Net framework,the S-UNet short-term precipitation nowcasting network is proposed,achieving remarkable performance on the KNMI dataset.Experimental results show that,compared with the mainstream methods,on the KNMI’s NL-50 and NL-20 datasets,the proposed method improves the Heidke skill score(HSS)and critical success index(CSI)by 5.25%(6.57%)and 2.17%(4.75%)respectively,reaching 0.30(0.29)and 0.72(0.58);the accuracy increases by 2.10%(1.35%),reaching 0.80(0.80);and the false acceptance rate decreases by 4.27%(1.80%),dropping to 0.24(0.38).Additionally,the effectiveness of the proposed modules and their combination methods are verified through ablation experiments.

关 键 词：短临降水预报 U-Net LSTM 深度学习 雷达回波 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TN957.52[自动化与计算机技术—控制科学与工程] P457.6[电子电信—信号与信息处理]