Sang‐Yeon Lee, Deuk-young Jeong, Jinseo Choi, Seng-Kyoun Jo, Dae-Heon Park, Jun-gyu Kim
{"title":"LSTM model to predict missing data of dissolved oxygen in land‐based aquaculture farm","authors":"Sang‐Yeon Lee, Deuk-young Jeong, Jinseo Choi, Seng-Kyoun Jo, Dae-Heon Park, Jun-gyu Kim","doi":"10.4218/etrij.2023-0337","DOIUrl":null,"url":null,"abstract":"A long short‐term memory (LSTM) model is introduced to predict missing datapoints of dissolved oxygen (DO) in an eel (Anguilla japonica) recirculating aquaculture system. Field experiments allow to determine periodic patterns in DO data corresponding to day–night cycles and a DO decrease after feeding. To improve the accuracy of DO prediction by using a training‐to‐test data ratio of 5:1, training with data in sequential and reverse orders is performed and evaluated. The LSTM model used to predict DO levels in the fish tank has an error of approximately 3.25%. The proposed LSTM model trained on DO data has a high applicability and may support water quality control in aquaculture farms.","PeriodicalId":11901,"journal":{"name":"ETRI Journal","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ETRI Journal","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.4218/etrij.2023-0337","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
A long short‐term memory (LSTM) model is introduced to predict missing datapoints of dissolved oxygen (DO) in an eel (Anguilla japonica) recirculating aquaculture system. Field experiments allow to determine periodic patterns in DO data corresponding to day–night cycles and a DO decrease after feeding. To improve the accuracy of DO prediction by using a training‐to‐test data ratio of 5:1, training with data in sequential and reverse orders is performed and evaluated. The LSTM model used to predict DO levels in the fish tank has an error of approximately 3.25%. The proposed LSTM model trained on DO data has a high applicability and may support water quality control in aquaculture farms.
期刊介绍：
ETRI Journal is an international, peer-reviewed multidisciplinary journal published bimonthly in English. The main focus of the journal is to provide an open forum to exchange innovative ideas and technology in the fields of information, telecommunications, and electronics.
Key topics of interest include high-performance computing, big data analytics, cloud computing, multimedia technology, communication networks and services, wireless communications and mobile computing, material and component technology, as well as security.
With an international editorial committee and experts from around the world as reviewers, ETRI Journal publishes high-quality research papers on the latest and best developments from the global community.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
