Utilizing a Two-Dimensional Data-Driven Convolutional Neural Network for Long-Term Prediction of Dissolved Oxygen Content

Abstract

It is significant to establish a precise dissolved oxygen (DO) model to obtain clear knowledge ablout the prospective changing conditions of the aquatic environment of marine ranches and to ensure the healthy growth of fisheries. However Do in marine ranches is affected by many factors. DO trends have complex nonlinear characteristics. Therefore, the accurate prediction of DO is challenging. On this basis, a two-dimensional data-driven convolutional neural network model (2DD-CNN) is proposed. In order to reduce the influence of missing values on experimental results, a novel sequence score matching-filling (SSMF) algorithm is first presented based on similar historical series matching to provide missing values. This paper extends the DO expression dimension and constructs a method that can convert a DO sequence into two-dimensional images and is also convenient for the 2D convolution kernel to further extract various pieces of information. In addition, a self-attention mechanism is applied to construct a CNN to capture the interdependent features of time series. Finally, DO samples from multiple marine ranches are validated and compared with those predicted by other models. The experimental results show that the proposed model is a suitable and effective method for predicting DO in multiple marine ranches. The MSE MAE, RMSE and MAPE of the 2DD-CNN prediction results are reduced by 51.63, 30.06, 32.53, and 30.75% on average, respectively, compared with those of other models, and the R2 is 2.68% higher on average than those of the other models. It is clear that the proposed 2DD-CNN model achieves a high forecast accuracy and exhibits good generalizability.