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Wu, Fanming, Li, Dengao, Zhao, Jumin, Jiang, Hairong, Luo, Xinyu (2024) SDIPPWV: A novel hybrid prediction model based on stepwise decomposition-integration-prediction avoids future information leakage to predict precipitable water vapor from GNSS observations. Science of The Total Environment, 933. 173116 doi:10.1016/j.scitotenv.2024.173116

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Reference TypeJournal (article/letter/editorial)
TitleSDIPPWV: A novel hybrid prediction model based on stepwise decomposition-integration-prediction avoids future information leakage to predict precipitable water vapor from GNSS observations
JournalScience of The Total Environment
AuthorsWu, FanmingAuthor
Li, DengaoAuthor
Zhao, JuminAuthor
Jiang, HairongAuthor
Luo, XinyuAuthor
Year2024 (July)Volume933
PublisherElsevier BV
DOIdoi:10.1016/j.scitotenv.2024.173116Search in ResearchGate
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Mindat Ref. ID17383726Long-form Identifiermindat:1:5:17383726:1
GUID0
Full ReferenceWu, Fanming, Li, Dengao, Zhao, Jumin, Jiang, Hairong, Luo, Xinyu (2024) SDIPPWV: A novel hybrid prediction model based on stepwise decomposition-integration-prediction avoids future information leakage to predict precipitable water vapor from GNSS observations. Science of The Total Environment, 933. 173116 doi:10.1016/j.scitotenv.2024.173116
Plain TextWu, Fanming, Li, Dengao, Zhao, Jumin, Jiang, Hairong, Luo, Xinyu (2024) SDIPPWV: A novel hybrid prediction model based on stepwise decomposition-integration-prediction avoids future information leakage to predict precipitable water vapor from GNSS observations. Science of The Total Environment, 933. 173116 doi:10.1016/j.scitotenv.2024.173116
In(2024) Science of The Total Environment Vol. 933. Elsevier BV

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