Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay

Jiye Lee; Ather Abbas; Gregory W. McCarty; Xuesong Zhang; Sangchul Lee; Kyung Hwa Cho

doi:10.1016/j.jhydrol.2022.128916

Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay

Jiye Lee, Ather Abbas, Gregory W. McCarty, Xuesong Zhang, Sangchul Lee^*, Kyung Hwa Cho

^*Corresponding author for this work

Physical Sciences and Engineering

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Original language	English (US)
Article number	128916
Journal	Journal of Hydrology
Volume	617
DOIs	https://doi.org/10.1016/j.jhydrol.2022.128916
State	Published - Feb 2023

Bibliographical note

Funding Information:
This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Aquatic Ecosystem Conservation Research Program, funded by Korea Ministry of Environment (MOE) (2020003050001) and the Basic Study and Interdisciplinary R&D Foundation Fund of the University of Seoul (2021) for SangChul Lee. Authors Drs. Gregory McCarty and Xuesong Zhang are supported by USDA and data presented in this paper were collected under the framework of the USDA Conservation Effects Assessment Project (CEAP) and the Long-Term Agroecosystem Research (LTAR) network.

ASJC Scopus subject areas

Water Science and Technology

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10.1016/j.jhydrol.2022.128916

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title = "Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay",

author = "Jiye Lee and Ather Abbas and McCarty, {Gregory W.} and Xuesong Zhang and Sangchul Lee and {Hwa Cho}, Kyung",

note = "Funding Information: This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Aquatic Ecosystem Conservation Research Program, funded by Korea Ministry of Environment (MOE) (2020003050001) and the Basic Study and Interdisciplinary R&D Foundation Fund of the University of Seoul (2021) for SangChul Lee. Authors Drs. Gregory McCarty and Xuesong Zhang are supported by USDA and data presented in this paper were collected under the framework of the USDA Conservation Effects Assessment Project (CEAP) and the Long-Term Agroecosystem Research (LTAR) network. ",

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AU - Abbas, Ather

AU - McCarty, Gregory W.

AU - Zhang, Xuesong

AU - Lee, Sangchul

AU - Hwa Cho, Kyung

N1 - Funding Information: This work was supported by Korea Environment Industry & Technology Institute (KEITI) through Aquatic Ecosystem Conservation Research Program, funded by Korea Ministry of Environment (MOE) (2020003050001) and the Basic Study and Interdisciplinary R&D Foundation Fund of the University of Seoul (2021) for SangChul Lee. Authors Drs. Gregory McCarty and Xuesong Zhang are supported by USDA and data presented in this paper were collected under the framework of the USDA Conservation Effects Assessment Project (CEAP) and the Long-Term Agroecosystem Research (LTAR) network.

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ER -

Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay

Bibliographical note

ASJC Scopus subject areas

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