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%0 Journal Article
%1 wong2021using
%A Wong, Pei-Yi
%A Lee, Hsiao-Yun
%A Chen, Yu-Cheng
%A Zeng, Yu-Ting
%A Chern, Yinq-Rong
%A Chen, Nai-Tzu
%A Lung, Shih-Chun Candice
%A Su, Huey-Jen
%A Wu, Chih-Da
%D 2021
%I Elsevier
%J Environmental Pollution
%K air lur pollution rf xgboost
%P 116846
%T Using a land use regression model with machine learning to estimate ground level PM2. 5
%V 277
@article{wong2021using,
added-at = {2022-04-06T16:25:46.000+0200},
author = {Wong, Pei-Yi and Lee, Hsiao-Yun and Chen, Yu-Cheng and Zeng, Yu-Ting and Chern, Yinq-Rong and Chen, Nai-Tzu and Lung, Shih-Chun Candice and Su, Huey-Jen and Wu, Chih-Da},
biburl = {https://www.bibsonomy.org/bibtex/2c7b8909ae0abcbd4d8607bfba39b651d/msteininger},
interhash = {560e1d7e9be653343e371d827d1c9d9a},
intrahash = {c7b8909ae0abcbd4d8607bfba39b651d},
journal = {Environmental Pollution},
keywords = {air lur pollution rf xgboost},
pages = 116846,
publisher = {Elsevier},
timestamp = {2022-04-06T16:25:46.000+0200},
title = {Using a land use regression model with machine learning to estimate ground level PM2. 5},
volume = 277,
year = 2021
}
