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An Assimilating Model Using Broad Learning System for Incorporating Multi-Source Precipitation Data With Environmental Factors Over Southeast China
Zhou, Yuanyuan1; Li, Xu2; Tang, Qiuhong3,4; Kuok, Sin Chi1; Fei, Kai1; Gao, Liang1
2022-04-01
Source PublicationEarth and Space Science
Volume9Issue:4
Abstract

Remote sensing technique is beneficial for rainfall data retrievals, however, enhancing the accuracy remains a challenge. In this study, a novel framework based on a broad learning system (BLS) was proposed to assimilate multi-source data. The dataset includes six satellite-based rainfall products (3B42V7, 3B42RT, IMERG, CBLD, GSMaP, and PCDR), gauge-based rainfall, and environmental data (temperature, specific humidity, wind speed, and locations) from 1 March 2014 to 31 December 2017 over southeast China (SEC). Leave-one-year-out cross-validation (LOYOCV) and independent validation were used to evaluate the BLS assimilating model. The proposed BLS model outperformed six original satellite-based products on Pearson's correlation coefficient (CC), root-mean-square error (RMSE), and Nash-Sutcliffe coefficient of efficiency (NSE) in each test year of LOYOCV. BLS model considering the environmental factors performed better on CC, RMSE, and NSE compared to that without environmental factors. Seasonal variations of daily gauge-based precipitation were accurately captured by BLS-based estimates. BLS method outperformed satellites on CC, RMSE, and NSE at most validation sites at low altitudes (0–1000 m). According to the independent validation, more accurate daily precipitation estimates could be obtained at more than half of the validation sites using the proposed model compared to the source datasets. The BLS-based framework considering environmental factors has the potential to improve estimates over SEC and is expected to be applied to other regions.

KeywordAssimilation Broad Learning System Environmental Factors Precipitation
DOI10.1029/2021EA002043
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaAstronomy & Astrophysics ; Geology
WOS SubjectAstronomy & Astrophysics ; Geosciences, Multidisciplinary
WOS IDWOS:000777950700001
Scopus ID2-s2.0-85128860513
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Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
Corresponding AuthorGao, Liang
Affiliation1.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macao
2.School of Civil Engineering, Beijing Jiaotong University, Beijing, China
3.Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
4.University of Chinese Academy of Sciences, Beijing, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Zhou, Yuanyuan,Li, Xu,Tang, Qiuhong,et al. An Assimilating Model Using Broad Learning System for Incorporating Multi-Source Precipitation Data With Environmental Factors Over Southeast China[J]. Earth and Space Science,2022,9(4).
APA Zhou, Yuanyuan,Li, Xu,Tang, Qiuhong,Kuok, Sin Chi,Fei, Kai,&Gao, Liang.(2022).An Assimilating Model Using Broad Learning System for Incorporating Multi-Source Precipitation Data With Environmental Factors Over Southeast China.Earth and Space Science,9(4).
MLA Zhou, Yuanyuan,et al."An Assimilating Model Using Broad Learning System for Incorporating Multi-Source Precipitation Data With Environmental Factors Over Southeast China".Earth and Space Science 9.4(2022).
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