Rainfall Data Reconstruction Based on Chaotic Characteristics of Meteorological Factors. Issue 1 (November 2020)

• Record Type: Journal Article
• Authors: Li, Junchen; Kong, Ke; Cui, Chenfeng; Zhang, Zhitao
• Is Part Of: IOP conference series. Volume 598:Issue 1(2020)
• Publication Date: 2020-11
• Subjects: Chaos Theory -- Refactoring Research -- Rainfall Data -- Temperature Data; Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/598/1/012027
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Rainfall data is a basic hydrological data, which is indispensable for the calculation of many hydrometeorological conditions. However, the acquisition of long-sequence rainfall data often has the problem of missing data. Based on the chaotic characteristics of rainfall and temperature data changes, the paper introduces temperature data with strong correlation with rainfall data into the model based on similar phase space theory for reconstruction of rainfall data. Through experiments in the Loess Plateau, the results show that the similarity between the calculated results and the real data reaches 95.17%. Compared with the traditional method, the accuracy of data reconstruction is improved.