Prediction of environmental factors in seafood cold chain transportation based on IAGA-ELM algorithm

Cold chain transportation is often used to ensure the quality of seafood. In order to reduce the impact of environmental factors on product quality during cold chain transportation, an environmental factor prediction method based on improved adaptive genetic algorithm (IAGA) and extreme learning machine (ELM) is developed to predict the environmental factors generated in cold chain transportation. The environmental factor data composed of the gases released in the decay mechanism of cold fresh seafood is collected for training ELM with fast training speed and strong generalisation ability. Furthermore, the prediction model of environmental factors in the process of seafood cold chain transportation is established. Because initial connection weights and threshold values of traditional ELM have the characteristics of randomness, IAGA is used to address this issue. Compared with the existing ELM, GA-ELM, AGA-ELM and other models, our experimental results show that the proposed model obtains higher prediction accuracy and lower error rate. Copyright © 2023 Inderscience Enterprises Ltd.