An Innovative Hybrid Approach to Forecasting Soluble Oxygen for Optimal Water Purification in Highly Concentrated Aquaculture

An important measure of the water's quality in an aquaculture setting is the concentration of dissolved oxygen. Disintegrating oxygen content prediction using conventional techniques is slow and inaccurate due to the complexity, nonlinearity, and dynamics of the process. This research develops a hybrid model that addresses these problems by combining the radiation gradient enhancement machine (LightGBM) with this simple rechargeable unit (Biru). The first step was to find the important parameters by using linear interpolation and smoothing. After removing superfluous variables, the LightGBM algorithm predicts dissolved oxygen in highly intensive aquaculture and establishes its relevance. Lastly, the attention approach was used to map the learning parameter matrices and weighting matrices, allowing various weights to be applied to the Biru's hidden states. The results show that the given prediction model can capture the upward trend of oxygen dissolution fluctuations over a 10-day period with a rate of accuracy reaching 96.28% in only 122 seconds. It takes the least amount of time to compare the model impacts of Biru-AAttention, LightGBM-GGRU, LightGBM-LSTM, as well as LightGBM-BBiru. The improved accuracy of its predictions makes it a valuable tool for controlling the water quality in intensive aquaculture.