基于流域日降水量图的相似性搜索方法

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。

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

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.