基于流域日降水量图的相似性搜索方法  

A rainfall similarity search method based on daily precipitation images of watershed

作　　者：余宇峰[1] 贺新固 张潇 万定生[1] 杨永杰 YU Yufeng;HE Xingu;ZHANG Xiao;WAN Dingsheng;YANG Yongjie(College of Computer Science and Software,Hohai University,Nanjing 211100,China;Bureau of Hydrology,Changjiang Water Resources Commission,Wuhan 443010,China)

机构地区：[1]河海大学计算机与软件学院,江苏南京211100 [2]长江水利委员会水文局,湖北武汉443010

出　　处：《河海大学学报（自然科学版）》2024年第2期19-27,共9页Journal of Hohai University(Natural Sciences)

基　　金：国家重点研发计划项目(2021YFB3900605);江苏省水利科技项目(2021065,2020014)。

摘　　要：为了提升降水量图相似性分析的精确度,提出了一种基于流域日降水量图的相似性搜索方法,该方法从降雨图像中提取日降水量、降雨空间分布和降雨中心特征,并分别计算各特征的相似距离,同时通过提出的归一化折旧累积增益改进粒子群优化的集合加权方法对3个特征的相似距离进行加权融合,作为降雨图像的相似性度量。嘉陵江流域实例验证表明:该方法能够更好地表征降水量图的时空特征,可快速地从降水量图中检索出相似的降雨过程。In order to improve the accuracy of similarity analysis of precipitation images,a rainfall similarity search method based on daily precipitation images of watershed is proposed.The algorithm first extracts the daily precipitation,precipitation distribution,precipitation center characteristics of the precipitation images,and calculates the similarity distance of each characteristic respectively.Then,an ensemble weighting method of normalized discounted cumulative gain-improved particle swarm optimization is proposed to weight and fuse the three extracted features as the similarity measure of precipitation image.The similarity search experiments of daily precipitation images on the Jialing River Basin illustrate that the method proposed in this paper can better characterize the spatiotemporal characteristics of the precipitation image and quickly discover similar rainfall processes from precipitation images.

关 键 词：降水量图 特征提取 相似性分析 多元特征距离融合 改进粒子群算法 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]