LSTM and GRU based Accurate Water Quality Prediction for Smart Aquaculture

Abstract

The fast and precise water quality prediction in smart aquaculture can assist the farmers in taking corrective actions before the ecological environment deteriorates. However, the empirical methods take high computation time and provide low accuracy in water quality prediction due to the non-linear and dynamic nature of water quality parameters. This research work proposes long short-term memory (LSTM) and gated recurrent unit (GRU) deep learning recurrent neural network (DL-RNN) models for aquaculture water quality prediction (A-WQP). This work also presents an extensive study about the impact of hyper-parameters (hp ) on the performance of the proposed DL-RNN models using two different water quality datasets. The experimental evaluations show that for the optimal sets of hyper-parameters, the proposed DL models offer superior prediction accuracy and computation efficiency.