Automatic and Accurate Prediction of Key Water Quality Parameters Based on SRU Deep Learning in Mariculture

In smart mariculture, an automatic and accurate prediction of key water quality parameters is a significant and challenge issue. This paper focuses on the prediction of pH and water temperature parameters in key water quality parameters. Firstly, the water quality parameters are preprocessed by improved method. Then, the Pearson correlation coefficient method is used to find the correlation between the water quality parameters. Finally, the SRU (Simple Recurrent Unit) deep learning model is used to establish a prediction model for the key water quality parameters, so as to achieve accurate prediction. Meanwhile, we also evaluate the prediction effect of prediction model built by RNN (Recurrent Neural Network) deep learning network. The experimental results show that the proposed prediction method has higher prediction accuracy than the method based on RNN when the time complexity is similar. The proposed method takes 11.3ms to predict every data in average, and the prediction accuracy can reach 98.91%.

I. Introduction

In aquaculture, water quality is one of the important factors that affect fish yield. The prediction of water quality can help farmers to understand the changes of water quality parameters and adjust the water quality in a timely manner, so that the fish population can grow in the most appropriate environment. At the same time, the pollution of drugs on aquatic products can be reduced as far as possible, so as to achieve green farming. At present, the water quality prediction method based on deep learning is the hot spot of research.