基于随机森林模型的长江流域分区多源融合降水模拟方法研究

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国家科技期刊平台
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中文摘要：基于3种卫星降水产品,提出了一种基于随机森林模型的长江流域分区多源融合降水模拟算法(FCM-RF算法).采用模糊C均值算法,结合地面观测站点资料对长江流域进行降水区域划分,引入降水比降刻画降水空间性,进一步通过普通克里金插值法优化融合结果,得到一套长江流域空间分辨率为0.25°×0.25°的多源融合降水产品,并对其进行了评估.结果表明:FCM-RF算法在长江流域具有良好的表现,可以有效提高原始卫星降水产品对于降水事件的捕捉能力,在验证站点模拟降水量与实测降水量的相关系数可达到0.76;FCM-RF算法在年际上具有相似变化特征,对于春秋季降水的敏感性较高,在夏季由于强降水影响表现欠佳,冬季由于雨量稀少、存在固态降水,呈现出误差小、相关系数较低的特点;FCM-RF算法在东南地区具有较强的降水捕捉能力,在青藏高原地区的准确性较低.

外文标题：Research on zoning multi-source fusion precipitation simulation method of the Yangtze River Basin based on random forest model

外文摘要：A multi-source fusion precipitation simulation method for the Yangtze River Basin based on the random forest model(FCM-RF algorithm)was proposed using three types of satellite precipitation products.Using the fuzzy C-means algorithm and ground observation station data,the precipitation regions in the Yangtze River Basin were divided.The precipitation ratio was introduced to characterize the spatial distribution of precipitation.The fusion results were further optimized using the ordinary Kriging interpolation method.A set of multi-source fusion precipitation products with a spatial resolution of 0.25 °×0.25 ° in the Yangtze River Basin were obtained and evaluated.The results show that the FCM-RF algorithm has good performance in the Yangtze River Basin,which can effectively improve the capture ability of original satellite precipitation products for precipitation events.The correlation coefficient between simulated precipitation and measured precipitation at the validation stations can reach 0.76.The FCM-RF algorithm has similar interannual variation characteristics and is highly sensitive to precipitation in spring and autumn.It performs poorly in summer due to heavy rainfall,and exhibits small errors and low correlation coefficients in winter due to sparse rainfall and solid precipitation.The FCM-RF algorithm has strong precipitation capture ability in the southeast region,but its accuracy is low in the Qinghai Tibet Plateau region.

外文关键词：

precipitation simulationfuzzy C-means algorithmrandom forest modelzoning multi-source fusion methodFCM-RF algorithmYangtze River Basin

作者：

宋蕾玥、张珂、晁丽君、李曦、牛杰帆、黄轶铭

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作者单位：

河海大学水文水资源学院,江苏南京 210098

河海大学水灾害防御全国重点实验室,江苏南京 210098

河海大学长江保护与绿色发展研究院,江苏南京 210098

中国气象局水文气象重点开放实验室,江苏南京 210024

水利部水利大数据重点实验室,江苏南京 210098

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关键词：

降水模拟模糊C均值算法随机森林模型分区多源融合方法 FCM-RF算法长江流域

基金：

国家重点研发计划中央高校基本科研业务费专项

项目编号：

2023YFC3006505B240203007

出版年：

2024

DOI：

10.3880/j.issn.1004-6933.2024.03.015

水资源保护

河海大学中国水利学会环境水利研究会

水资源保护

CSTPCD北大核心EI

影响因子：0.827

ISSN：1004-6933

年,卷(期)：2024.40(3)

参考文献量31