Study on the Impact of Input Parameters on Seawater Dissolved Oxygen Prediction Models

The concentration of dissolved oxygen (DO) in seawater is a core ecological indicator in aquaculture, and its accurate prediction is of great value for the management of marine ranching. In response to the lack of exploration on the optimization mechanism of input parameters in existing DO prediction studies, this study, based on observational data from the Goji Island marine ranching, constructed a technical framework of “parameter screening—model optimization—ecological analysis”. By integrating correlation analysis, principal component analysis (PCA), and multi-model comparison (SVM, MLP, and RF) methods, this study systematically revealed the input parameter optimization strategies and its ecological correlation mechanism. The research findings are as follows: (1) Parameter optimization can significantly improve model accuracy, and the model performance is optimal after eliminating the low-correlation parameter (Tur) (RMSE = 0.039, MAE = 0.030, R2 = 0.884). (2) The absence of key parameters (such as Sal) will lead to a significant decrease in prediction accuracy (the R2 reduction rate reaches 71.6%). (3) The parameter importance ranking is Tem > pH > Sal > Chl-a > Tur, among which Tem explains 42.3% of the variation in DO. The intelligent parameter optimization framework proposed in this study provides theoretical support for the development of a marine ranching DO monitoring system, and its technical path can be extended to the prediction of other water environment indicators. Future research will develop a parameter adaptive selection algorithm, conduct the dynamic monitoring of multi-scale environmental factors, and achieve the intelligent optimization and verification of model parameters.