LSTM model to predict missing data of dissolved oxygen in land-based aquaculture farm

LSTM model to predict missing data of dissolved oxygen in land-based aquaculture farm

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 feedi...

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Bibliographic Details
Main Authors: Sang-Yeon Lee, Deuk-Young Jeong, Jinseo Choi, Seng-Kyoun Jo, Dae-Heon Park, Jun-Gyu Kim
Format: Article
Language:English
Published: Electronics and Telecommunications Research Institute (ETRI) 2024-12-01
Series:ETRI Journal
Subjects:
aquaculture farm
data imputation
dissolved oxygen
machine learning
recurrent neural network
Online Access:https://doi.org/10.4218/etrij.2023-0337
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https://doi.org/10.4218/etrij.2023-0337

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