Designing Forecasting Parameter Algorithm of Environmental Shrimp Using Recurrent Neural Network

With the strong development of science and technology, the study of technologies related to environmental forecasting is important. In recent years, the application of smart technology in aquaculture has been widely applied. Based on the requirement, we focus on predicting the environmental parameters applied in shrimp farming, especially white shrimp, one of the seafood grown in our country. In the paper, we exploit a small branch of identification problem. This paper proposes an algorithmic construction method to predict changes in shrimp farm environmental parameters and simulate the next parameters based on current parameters. The goal of the paper is to reduce the parameter of Recurrent Neural Network (RNN) while ensuring data accuracy. Experimental results show that the proposal algorithm improves up to 85 percent when selecting suitable learning factor of neural networks.