LSTM-TCN: dissolved oxygen prediction in aquaculture, based on combined model of long short-term memory network and temporal convolutional network.

Dissolved oxygen (DO) is an important water quality monitoring parameter of great significance in aquaculture. Accurate prediction of dissolved oxygen can help farmers to take necessary measures in advance to ensure the healthy growth of cultured species. The characteristics of multivariate and long-term correlation of water quality time series in the traditional methods make it difficult to achieve the expected prediction accuracy. To solve this problem, we propose the combined prediction method LSTM-TCN (long short-term memory network and temporal convolutional network). After the preprocessing of time series, the LSTM extracts the features of the series in time dimension, and then combines with TCN to build the fusion prediction model. In this study, we have carried out the DO predictions of LSTM and TCN algorithms separately, followed by the analysis of DO prediction, based on CNN-LSTM and LSTM-TCN combined models. The effects of attention mechanism and window size of historical time on the prediction results were also investigated. The experimental results show that the combined method has high accuracy in dissolved oxygen prediction, and can capture better characteristics of historical data with increasing time window of the historical dissolved oxygen sequence. The LSTM-TCN method achieves better prediction performance, with evaluation index values of MAE = 0.236, MAPE = 3.10%, RMSE = 0.342, and R2 = 0.94.

Review of Dissolved Oxygen Detection Technology: From Laboratory Analysis to Online Intelligent Detection.

Dissolved oxygen is an important index to evaluate water quality, and its concentration is of great significance in industrial production, environmental monitoring, aquaculture, food production, and other fields. As its change is a continuous dynamic process, the dissolved oxygen concentration needs to be accurately measured in real time. In this paper, the principles, main applications, advantages, and disadvantages of iodometric titration, electrochemical detection, and optical detection, which are commonly used dissolved oxygen detection methods, are systematically analyzed and summarized. The detection mechanisms and materials of electrochemical and optical detection methods are examined and reviewed. Because external environmental factors readily cause interferences in dissolved oxygen detection, the traditional detection methods cannot adequately meet the accuracy, real-time, stability, and other measurement requirements; thus, it is urgent to use intelligent methods to make up for these deficiencies. This paper studies the application of intelligent technology in intelligent signal transfer processing, digital signal processing, and the real-time dynamic adaptive compensation and correction of dissolved oxygen sensors. The combined application of optical detection technology, new fluorescence-sensitive materials, and intelligent technology is the focus of future research on dissolved oxygen sensors.