Marine chlorophyll-a prediction based on deep auto-encoded temporal convolutional network model

Abstract

Chlorophyll-a concentration is one of the important indicators for assessing marine ecology. Accurate prediction of marine chlorophyll-a concentrations is a prerequisite for early warning of marine hazards such as red tide. There are many factors affecting chlorophyll-a concentration. Marine water quality parameter data as a time series is non-linear. There is a strong coupling relationship between various water quality parameters. This makes the prediction difficult. Therefore, there is a need to conduct research on marine chlorophyll-a concentration prediction methods. In this study, a depth-automatically encoded temporal convolutional network (DAE-TCN) is proposed to predict marine chlorophyll-a concentrations. Specifically, the DAE-TCN algorithm performs spatial feature extraction and data dimensionality reduction on the data through encoding to alleviate the difficulty of temporal feature extraction by temporal convolution, improve the efficiency of data information utilization, and obtain accurate prediction results. In this paper, we use the data from the coast of Beihai, Guangxi, as the experimental data. The DAE-TCN algorithm is compared with three baseline algorithms, and the results indicate that the algorithm shows advantages in stability and prediction accuracy and can be well applied to marine chlorophyll-a concentration prediction. This study has scientific significance for marine environmental protection.