Full metadata record
DC Field | Value | Language |
---|---|---|
dc.citation.number | 12 | - |
dc.citation.startPage | 3399 | - |
dc.citation.title | WATER | - |
dc.citation.volume | 12 | - |
dc.contributor.author | Baek, Sang-Soo | - |
dc.contributor.author | Pyo, Jongcheol | - |
dc.contributor.author | Chun, Jong Ahn | - |
dc.date.accessioned | 2023-12-21T16:37:45Z | - |
dc.date.available | 2023-12-21T16:37:45Z | - |
dc.date.created | 2021-02-23 | - |
dc.date.issued | 2020-12 | - |
dc.description.abstract | A Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) combined with a deep learning approach was created by combining CNN and LSTM networks simulated water quality including total nitrogen, total phosphorous, and total organic carbon. Water level and water quality data in the Nakdong river basin were collected from the Water Resources Management Information System (WAMIS) and the Real-Time Water Quality Information, respectively. The rainfall radar image and operation information of estuary barrage were also collected from the Korea Meteorological Administration. In this study, CNN was used to simulate the water level and LSTM used for water quality. The entire simulation period was 1 January 2016-16 November 2017 and divided into two parts: (1) calibration (1 January 2016-1 March 2017); and (2) validation (2 March 2017-16 November 2017). This study revealed that the performances of both of the CNN and LSTM models were in the "very good" range with above the Nash-Sutcliffe efficiency value of 0.75 and that those models well represented the temporal variations of the pollutants in Nakdong river basin (NRB). It is concluded that the proposed approach in this study can be useful to accurately simulate the water level and water quality. | - |
dc.identifier.bibliographicCitation | WATER, v.12, no.12, pp.3399 | - |
dc.identifier.doi | 10.3390/w12123399 | - |
dc.identifier.issn | 2073-4441 | - |
dc.identifier.scopusid | 2-s2.0-85097309428 | - |
dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/50050 | - |
dc.identifier.url | https://www.mdpi.com/2073-4441/12/12/3399 | - |
dc.identifier.wosid | 000602946900001 | - |
dc.language | 영어 | - |
dc.publisher | MDPI | - |
dc.title | Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach | - |
dc.type | Article | - |
dc.description.isOpenAccess | TRUE | - |
dc.relation.journalWebOfScienceCategory | Environmental Sciences; Water Resources | - |
dc.relation.journalResearchArea | Environmental Sciences & Ecology; Water Resources | - |
dc.type.docType | Article | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | water quality prediction | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | convolutional neural network (CNN) | - |
dc.subject.keywordAuthor | long short-term memory (LSTM) network | - |
dc.subject.keywordPlus | RIVER | - |
dc.subject.keywordPlus | NITROGEN | - |
dc.subject.keywordPlus | MODEL | - |
dc.subject.keywordPlus | RAINFALL | - |
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.