A Comparative Study of LSTM, GRU, BiLSTM and BiGRU to Predict Dissolved Oxygen
Narongsak Putpuek, Apiradee Putpuek, A. Sungthong
Abstract
In aquaculture, dissolved oxygen (DO) levels affect fish growth and survival. Automated monitoring and prediction of DO is challenging and becomes expensive if unnecessary sensors are used. This study aims to identify the optimal water and environmental parameters for DO prediction. Data from the fishpond station of Rajabhat Rajanagarindra University were pre-processed and used for training using LSTM, GRU, BiLSTM, and BiGRU. The performance of the models was evaluated and contrasted using three error measures. The results showed that GRU gave the best performance compared to the other models. In conclusion, the best parameters for DO prediction are water pH and water temperature.
Topics & Concepts
Mean squared prediction errorComputer scienceAquacultureEnvironmental scienceFish <Actinopterygii>Data modelingArtificial intelligenceEnvironmental engineeringMachine learningFisheryBiologyDatabaseWater Quality Monitoring TechnologiesWater Quality Monitoring and AnalysisHydrological Forecasting Using AI