An Enhanced Naive Bayes Model for Dissolved Oxygen Forecasting in Shellfish Aquaculture

It is difficult to predict dissolved oxygen values because they are disordered and nonlinear. Accurate prediction of dissolved oxygen in shellfish aquaculture plays an important role in improving shellfish production, and a reliable model is needed to accurately predict dissolved oxygen values. Therefore, in this paper, an enhanced naive Bayes (NB) model is proposed. Due to the excessive number of different dissolved oxygen values, their direct use as input samples will result in overly few training set categories for each value, which reduces the prediction accuracy. Therefore, the dissolved oxygen differential series dataset is used as the input data to reduce the number of training set categories and improve the training accuracy. To increase the number of samples in the training set, the sliding window concept from network communication protocols is used to partition the differential sequence dataset and generate the features and labels of the training set. The values were predicted as categories, and the dissolved oxygen data were accurately predicted by selecting the labels that correspond to the posterior probability maxima of all training samples. Finally, the algorithm is used to predict the dissolved oxygen data from February 18, 2016, to January 31, 2020, in Yantai, Shandong Province, China. The dissolved oxygen data of a shellfish farm were trained and predicted, and the best values of the feature lengths were optimized by analyzing their effects on the predicted dissolved oxygen values. The proposed algorithm has significantly improved the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) compared to the advanced algorithms. The results of the Diebold-Mariano test and 10-fold cross-validation also show that the proposed algorithm has a higher prediction accuracy.