Prediction of dissolved oxygen content in river crab culture based on least squares support vector regression optimized by improved particle swarm optimization

• This paper presents a dissolved oxygen content prediction model based on IPSO-LSSVR.
• IPSO as a global optimizer is employed to optimize the hyper-parameters of LSSVR model.
• IPSO-LSSVR has a better performance for prediction of dissolved oxygen content.
• IPSO-LSSVR yields better prediction results than standard LSSVR and BP neural network.
• IPSO-LSSVR is an effective prediction model tool for water quality in the aquaculture.

It is important to set up a precise predictive model to obtain clear knowledge of the prospective changing conditions of dissolved oxygen content in intensive aquaculture ponds and to reduce the financial losses of aquaculture. This paper presents a hybrid dissolved oxygen content prediction model based on the least squares support vector regression (LSSVR) model with optimal parameters selected by improved particle swarm optimization (IPSO) algorithm. In view of the slow convergence of particle swarm algorithm (PSO), improved PSO with the dynamically adjusted inertia weight was based on the fitness function value to improve convergence. Then a global optimizer, IPSO, was employed to optimize the hyperparameters needed in the LSSVR model. We adopted an IPSO-LSSVR algorithm to construct a non-linear prediction model. IPSO-LSSVR was tested and compared to other algorithms by applying it to predict dissolved oxygen content in river crab culture ponds. Experiment results show that the proposed model of IPSO-LSSVR could increase the prediction accuracy and execute generalization performance better than the standard support vector regression (SVR) and BP neural network, and it is a suitable and effective method for predicting dissolved oxygen content in intensive aquaculture.