There are no files associated with this item.
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.endPage | 3039 | - |
dc.citation.number | 7 | - |
dc.citation.startPage | 3021 | - |
dc.citation.title | GEOSCIENTIFIC MODEL DEVELOPMENT | - |
dc.citation.volume | 15 | - |
dc.contributor.author | Abbas, Ather | - |
dc.contributor.author | Boithias, Laurie | - |
dc.contributor.author | Pachepsky, Yakov | - |
dc.contributor.author | Kim, Kyunghyun | - |
dc.contributor.author | Chun, Jong Ahn | - |
dc.contributor.author | Cho, Kyung Hwa | - |
dc.date.accessioned | 2023-12-21T14:15:14Z | - |
dc.date.available | 2023-12-21T14:15:14Z | - |
dc.date.created | 2022-05-19 | - |
dc.date.issued | 2022-04 | - |
dc.description.abstract | Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine-learning-based hydrological models requires advanced skills from diverse fields, such as programming and hydrological modeling. Additionally, data pre-processing and post-processing when training and testing machine learning models are a time-intensive process. In this study, we developed a python-based framework that simplifies the process of building and training machine-learning-based hydrological models and automates the process of pre-processing hydrological data and post-processing model results. Pre-processing utilities assist in incorporating domain knowledge of hydrology in the machine learning model, such as the distribution of weather data into hydrologic response units (HRUs) based on different HRU discretization definitions. The post-processing utilities help in interpreting the model's results from a hydrological point of view. This framework will help increase the application of machine-learning-based modeling approaches in hydrological sciences. | - |
dc.identifier.bibliographicCitation | GEOSCIENTIFIC MODEL DEVELOPMENT, v.15, no.7, pp.3021 - 3039 | - |
dc.identifier.doi | 10.5194/gmd-15-3021-2022 | - |
dc.identifier.issn | 1991-959X | - |
dc.identifier.scopusid | 2-s2.0-85128778783 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/58565 | - |
dc.identifier.url | https://gmd.copernicus.org/articles/15/3021/2022/ | - |
dc.identifier.wosid | 000792360200001 | - |
dc.language | 영어 | - |
dc.publisher | COPERNICUS GESELLSCHAFT MBH | - |
dc.title | AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods | - |
dc.type | Article | - |
dc.description.isOpenAccess | FALSE | - |
dc.relation.journalWebOfScienceCategory | Geosciences, Multidisciplinary | - |
dc.relation.journalResearchArea | Geology | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | IMPACT | - |
Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Tel : 052-217-1404 / Email : scholarworks@unist.ac.kr
Copyright (c) 2023 by UNIST LIBRARY. All rights reserved.
ScholarWorks@UNIST was established as an OAK Project for the National Library of Korea.