BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data

Complete and high-resolution temperature observation data are important input parameters for agrometeorological disaster monitoring and ecosystem modelling. Due to the limitation of field meteorological observation conditions, observation data are commonly missing, and an appropriate data imputation...

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Detalles Bibliográficos
Autores principales: Xie, Chuanjie, Huang, Chong, Zhang, Deqiang, He, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507855/
https://www.ncbi.nlm.nih.gov/pubmed/34639622
http://dx.doi.org/10.3390/ijerph181910321

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8507855/
https://www.ncbi.nlm.nih.gov/pubmed/34639622
http://dx.doi.org/10.3390/ijerph181910321

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