Attention-driven LSTM and GRU deep learning techniques for precise water quality prediction in smart aquaculture.

Global food security, economic growth, and biodiversity preservation are impacted significantly by aquaculture. Water quality monitoring (WQM) and water quality prediction (WQP) are essential for profitable as well as sustainable aquaculture. Empirical techniques lead to erroneous WQP, which has a negative impact on aquaculture by generating disease outbreaks, oxygen depletion, nutrient imbalances, chemical pollution, and unfavorable environmental effects. In this work, we propose attention-driven long short-term memory (A-LSTM) and gated recurrent unit (A-GRU) deep learning recurrent neural network (DL-RNN) models for aquaculture WQP. This study utilizes two datasets. The first dataset consists of 3 years of data with 1096 samples collected from aquaculture farms under the Agency for Development of Aquaculture Kerala (ADAK) in India. The second dataset is publicly available, where data is collected from the marine aquaculture base in Xincun Town, LingShui County, Hainan Province, China, which consists of 23200 samples collected over 80 days. Additionally, this work presents a thorough analysis of the effects of hyperparameters ( h p ) on the performance of the proposed models using two different water quality datasets. The prediction performance of proposed A-LSTM as well as A-GRU are compared with conventional LSTM and GRU DL-RNN models in terms of prediction accuracy and computational efficiency. Prediction accuracy in the range of 98.30 to 99.70% is observed for various water quality parameters. The findings demonstrate that the proposed A-LSTM and A-GRU models significantly improve prediction accuracy with lesser computation time. [ABSTRACT FROM AUTHOR]