A prediction model for chlorophyll concentration in seawater based on BorutaShap-MEMD-GRU

Abstract

Abstract To solve the problem of the difficulty in selecting multi-parameter features of the ocean and the lack of power of traditional time-series prediction models in predicting ocean data, an ocean time-series prediction GRU model based on the Borutashap algorithm, and a hybrid multivariate empirical modal decomposition is proposed to predict multivariate in this paper. The feature selection of multi-feature ocean data is carried out by the Borutashap algorithm based on the XG-boost model, then the selected data are decomposed by multi-modal decomposition, and the data are reconstructed to get the high-frequency and low-frequency components, and the trend term components by Permutation Entropy, and finally the high-frequency and low-frequency components and the trend term are respectively brought into the GRU network and the prediction is summed up to get the final result. In this paper, the model’s effectiveness is verified by ablation experiments and compared with other feature selection models and classical time series prediction models, the results show that the model has a better prediction effect.