AI-HydSu: An advanced hybrid approach using support vector regression and particle swarm optimization for dissolved oxygen forecasting

Since the variations in the dissolved oxygen concentration are affected by many factors, the corresponding uncertainty is nonlinear and fuzzy. Therefore, the accurate prediction of dissolved oxygen concentrations has been a difficult problem in the fishing industry. To address this problem, a hybrid dissolved oxygen concentration prediction model (AI-HydSu) is proposed in this paper. First, to ensure the accuracy of the experimental results, the data are preprocessed by wavelet threshold denoising, and the advantages of the particle swarm optimization (PSO) algorithm are used to search the solution space and select the best parameters for the support vector regression (SVR) model. Second, the prediction model optimizes the invariant learning factors in the standard PSO algorithm by using nonlinear adaptive learning factors, thus effectively preventing the algorithm from falling to local optimal solutions and accelerating the algorithm's optimization search process. Third, the velocities and positions of the particles are updated by constantly updating the learning factors to finally obtain the optimal combination of SVR parameters. The algorithm not only performs searches for the penalty factor, kernel function parameters, and error parameters in SVR but also balances its global and local search abilities. A dissolved oxygen concentration prediction experiment demonstrates that the proposed model achieves high accuracy and a fast convergence rate.

Automated summary

A hybrid dissolved oxygen concentration prediction model (AI-HydSu) is proposed, which improves the accuracy and convergence rate of dissolved oxygen concentration prediction through data preprocessing and optimizing learning factors.