中国水利Issue(20)：37-46,10.DOI:10.3969/j.issn.1000-1123.2025.20.006
X波段水利测雨雷达降雨反演模型在湖南省的研究与应用
Research and application of X-band hydrological radar rainfall retrieval model in Hunan Province
摘要
Abstract
To address the limitations of traditional rain gauge networks,such as extensive monitoring blind spots and insufficient spatiotemporal resolution,in mountain flood prevention,this study develops a high-precision rainfall retrieval model based on X-band dual-polarization hydrological radar using observational data from four radars in Hunan Province.Combining traditional empirical modeling with machine learning techniques,a multi-parameter joint retrieval approach was established.In the empirical models,relationships between reflectivity(ZH),specific differential phase(KDP),and differential reflectivity(ZDR)and rainfall intensity were constructed to estimate precipitation.The machine learning models applied algorithms including k-nearest neighbor(KNN),support vector regression(SVR),random forest(RF),artificial neural network(ANN),and recurrent neural network(RNN)for rainfall fitting.Results show that among the empirical models,the ZH-R model performs best when rainfall intensity is below 10 mm/h,whereas the KDP-R model performs better when it exceeds 10 mm/h.Among the machine learning models,the RNN achieves the best performance,with a root mean square error(RMSE)of 3.125,a coefficient of determination(R2)of 0.981,and a Pearson correlation coefficient of 0.992,outperforming both other machine learning and empirical models.The integration of X-band radar with multi-parameter correction and machine learning techniques significantly enhances rainfall retrieval accuracy,providing more reliable data support for the"three defense lines"system in mountain flood disaster prevention.关键词
三道防线/测雨雷达/降雨反演/循环神经网络/山洪灾害/经验模型/机器学习模型Key words
three defense lines/rainfall radar/rainfall retrieval/recurrent neural network/mountain flood disaster/empirical model/machine learning model分类
水利科学引用本文复制引用
何秉顺,赵延伟..X波段水利测雨雷达降雨反演模型在湖南省的研究与应用[J].中国水利,2025,(20):37-46,10.基金项目
中国水利水电科学研究院"五大人才"项目(WH0145B062022). （WH0145B062022）
