Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States

10.1016/j.ejrh.2021.100930

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Bibliographic Details
Main Authors:	Cai, Hejiang, Shi, Haiyun, Liu, Suning, Babovic, Vladan
Other Authors:	COLLEGE OF DESIGN AND ENGINEERING
Format:	Article
Published:	Elsevier B.V. 2022
Subjects:	Central eastern continental United States Detrended fluctuation analysis Groundwater level Machine learning Principal component analysis Regional characteristics
Online Access:	https://scholarbank.nus.edu.sg/handle/10635/233122
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Institution:	National University of Singapore

id	sg-nus-scholar.10635-233122
record_format	dspace
spelling	sg-nus-scholar.10635-2331222022-10-13T07:32:53Z Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States Cai, Hejiang Shi, Haiyun Liu, Suning Babovic, Vladan COLLEGE OF DESIGN AND ENGINEERING Central eastern continental United States Detrended fluctuation analysis Groundwater level Machine learning Principal component analysis Regional characteristics 10.1016/j.ejrh.2021.100930 Journal of Hydrology: Regional Studies 37 100930 2022-10-13T07:32:47Z 2022-10-13T07:32:47Z 2021-10-01 Article Cai, Hejiang, Shi, Haiyun, Liu, Suning, Babovic, Vladan (2021-10-01). Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States. Journal of Hydrology: Regional Studies 37 : 100930. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ejrh.2021.100930 2214-5818 https://scholarbank.nus.edu.sg/handle/10635/233122 Attribution-NonCommercial-NoDerivatives 4.0 International https://creativecommons.org/licenses/by-nc-nd/4.0/ Elsevier B.V. Scopus OA2021
institution	National University of Singapore
building	NUS Library
continent	Asia
country	Singapore Singapore
content_provider	NUS Library
collection	ScholarBank@NUS
topic	Central eastern continental United States Detrended fluctuation analysis Groundwater level Machine learning Principal component analysis Regional characteristics
spellingShingle	Central eastern continental United States Detrended fluctuation analysis Groundwater level Machine learning Principal component analysis Regional characteristics Cai, Hejiang Shi, Haiyun Liu, Suning Babovic, Vladan Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States
description	10.1016/j.ejrh.2021.100930
author2	COLLEGE OF DESIGN AND ENGINEERING
author_facet	COLLEGE OF DESIGN AND ENGINEERING Cai, Hejiang Shi, Haiyun Liu, Suning Babovic, Vladan
format	Article
author	Cai, Hejiang Shi, Haiyun Liu, Suning Babovic, Vladan
author_sort	Cai, Hejiang
title	Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States
title_short	Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States
title_full	Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States
title_fullStr	Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States
title_full_unstemmed	Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States
title_sort	impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: the case of central eastern continental united states
publisher	Elsevier B.V.
publishDate	2022
url	https://scholarbank.nus.edu.sg/handle/10635/233122
_version_	1749178988475973632

Impacts of regional characteristics on improving the accuracy of groundwater level prediction using machine learning: The case of central eastern continental United States

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