Aquaculture Prediction Model Based on Improved Water Quality Parameter Data Prediction Algorithm under the Background of Big Data

Abstract

Computer science and technology under the background of big data are closely related to the development of modern agriculture. The application of information processing technology in aquaculture will promote the scientific development of aquaculture. The aquaculture water quality directly affects the effect of aquaculture. Therefore, on the basis of the dynamic monitoring model of water quality, the relevant factors affecting water quality were analyzed, and a prediction model of aquaculture water quality was constructed. Considering the complex relationship between dissolved oxygen and water quality, combined with principal component analysis, a PCA-BP (principal component analysis back propagation) water quality prediction model was proposed. The parameters of PCA-BP water quality prediction model were optimized by genetic algorithm, the threshold and weight of BP neural network were determined, and an improved PCA-BP water quality prediction model was constructed. The experimental results show that the relative error of the GPCA-BP water quality prediction model for the prediction of dissolved oxygen content is less than 0.76% in water quality prediction experiments in different times and regions, and it has the best prediction accuracy. At the same time, GPCA-BP water quality prediction model also has excellent performance in convergence accuracy, prediction accuracy, and MAE error performance test. The research content has important reference value for the application of information technology in modern aquaculture.