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

Dashe Li; Xueying Wang; Jiajun Sun; Huanhai Yang; Dashe Li; Xueying Wang; Jiajun Sun; Huanhai Yang

doi:10.3934/mbe.2021182

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 4: 3646-3666. doi: 10.3934/mbe.2021182

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Research article

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

1.
School of Computer Science and Technology, Shandong Technology and Business University, Yantai 264005, China
2.
Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Yantai 264005, China

Academic Editor: Yang Kuang

Received: 07 March 2021 Accepted: 19 April 2021 Published: 26 April 2021

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.
- support vector regression,
- particle swarm optimization,
- dissolved oxygen,
- adaptive,
- hybrid forecasting model
Citation: Dashe Li, Xueying Wang, Jiajun Sun, Huanhai Yang. AI-HydSu: An advanced hybrid approach using support vector regression and particle swarm optimization for dissolved oxygen forecasting[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 3646-3666. doi: 10.3934/mbe.2021182

Related Papers:

Abstract

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.

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