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

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.

Keywords

• aquaculture farm
• data imputation
• dissolved oxygen
• machine learning
• recurrent neural network