Open AccessRainfall Data Reconstruction Based on Chaotic Characteristics of Meteorological Factors
Author(s) -
Junchen Li,
Kong Ke,
Chenfeng Cui,
Zhitao Zhang
Publication year - 2020
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/598/1/012027
Subject(s) - hydrometeorology , chaotic , similarity (geometry) , loess plateau , meteorology , data mining , computer science , environmental science , climatology , precipitation , geology , geography , artificial intelligence , soil science , image (mathematics)
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.