Machine learning approach for the estimation of missing precipitation data: a case study of South Korea.

Han H, Kim B, Kim K, Kim D, Kim HS

Open source

DOI: 10.2166/wst.2023.237
Published: 2023 Aug
Container: Water science and technology : a journal of the International Association on Water Pollution Research
Publisher: Not recorded
Open access: no

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Cite this work

BibTeX

@article{allodium:10.2166/wst.2023.237,
  title = {Machine learning approach for the estimation of missing precipitation data: a case study of South Korea.},
  author = {Han H and Kim B and Kim K and Kim D and Kim HS},
  year = {2023},
  journal = {Water science and technology : a journal of the International Association on Water Pollution Research},
  doi = {10.2166/wst.2023.237},
  url = {https://doi.org/10.2166/wst.2023.237}
}

RIS

TY  - JOUR
TI  - Machine learning approach for the estimation of missing precipitation data: a case study of South Korea.
AU  - Han H
AU  - Kim B
AU  - Kim K
AU  - Kim D
AU  - Kim HS
PY  - 2023
JO  - Water science and technology : a journal of the International Association on Water Pollution Research
DO  - 10.2166/wst.2023.237
UR  - https://doi.org/10.2166/wst.2023.237
ER  -

APA

H, H., B, K., K, K., D, K., & HS, K. (2023). Machine learning approach for the estimation of missing precipitation data: a case study of South Korea.. Water science and technology : a journal of the International Association on Water Pollution Research. https://doi.org/10.2166/wst.2023.237

Source records

pubmed · retrieved 2026-09-25T02:17:29.484Z
europe-pmc · retrieved 2026-09-25T02:17:29.500Z
doaj · retrieved 2026-09-25T02:17:29.493Z