Time Series Prediction of Wastewater Flow Rate by Bidirectional LSTM Deep Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Hoon | - |
dc.contributor.author | Yang, Seunghyeok | - |
dc.contributor.author | Huang, Jianying | - |
dc.contributor.author | Oh, Jeill | - |
dc.date.accessioned | 2021-12-16T02:40:41Z | - |
dc.date.available | 2021-12-16T02:40:41Z | - |
dc.date.issued | 2020-12 | - |
dc.identifier.issn | 1598-6446 | - |
dc.identifier.issn | 2005-4092 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/52496 | - |
dc.description.abstract | This paper not only addresses a feasible strategy in predicting time series or sequences by using deep neural nets such as bi-LSTM (bidirectional Long Short-Term Memory), but also demonstrates fairly good results of forecasting wastewater flow rate for a municipal wastewater treatment plant in a practical sense. The basic procedures of time series prediction by deep learning are to collect the past information of all available states for deep learning and to utilize p-step ahead delays of a no-training interval with a sliding time window. Therefore, the sequence-to-point p-step prediction of sewage flow of Yangju wastewater treatment plant could be made possible by using bi-LSTM in accordance with this fundamental principle. | - |
dc.format.extent | 8 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS | - |
dc.title | Time Series Prediction of Wastewater Flow Rate by Bidirectional LSTM Deep Learning | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/s12555-019-0984-6 | - |
dc.identifier.bibliographicCitation | INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.18, no.12, pp 3023 - 3030 | - |
dc.identifier.kciid | ART002648317 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000599863700005 | - |
dc.identifier.scopusid | 2-s2.0-85097653147 | - |
dc.citation.endPage | 3030 | - |
dc.citation.number | 12 | - |
dc.citation.startPage | 3023 | - |
dc.citation.title | INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | - |
dc.citation.volume | 18 | - |
dc.type.docType | Article | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | bidirectional LSTM | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | neural net | - |
dc.subject.keywordAuthor | prediction | - |
dc.subject.keywordAuthor | rainfall | - |
dc.subject.keywordAuthor | time series | - |
dc.subject.keywordAuthor | wastewater treatment plant | - |
dc.subject.keywordAuthor | water flow rate | - |
dc.subject.keywordPlus | PHONEME CLASSIFICATION | - |
dc.subject.keywordPlus | NETWORKS | - |
dc.subject.keywordPlus | INFLOW | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.