Prediction model of dissolved oxygen based on FOA-LSSVR

Abstract

Dissolved oxygen directly affects the growth status of fishes in intensive aquaculture, thus we set up a prediction model to determine the future changing trend of dissolved oxygen. The dissolved oxygen prediction model we proposed was based on the least squares support vector regression (LSSVR) model with fruit fly optimization algorithm (FOA) to find optimal parameters (γ and σ) of LSSVR. Because these two parameters can significantly affect the performance of the LSSVR, we studied the other two parameter optimization methods the particle swarm optimization (PSO) algorithm, the genetic algorithm (GA) and immune genetic algorithm(IGA) to compare them with the FOA algorithm. The calculated mean absolute percentage errors of the results of the four prediction models were 0.35%, 1.3%, 2.03% and 1.33%, respectively. The FOA-LSSVR model has a higher prediction accuracy and more reliable performance than the other models. When the predicted values of dissolved oxygen fall below the safety level, the farmer can start an oxygen increasing machine in advance to maintain the safety of fishes. The prediction model was used in Yangzhong, Jiangsu province, China, and it performed well and helped farmers make decisions and reduce aquaculture risks.