Multi-Step Forecasting of Chlorophyll Concentration with Multi-Attention Collaborative Network

Abstract

In a marine environment, the concentration of chlorophyll is an important indicator of quality, which is also considered an indicator used to predict the marine ecological environment, which is further considered an important means of predicting red tide disasters. Although existing methods for predicting chlorophyll concentration have achieved encouraging performance, there are still two limitations: (i) they primarily focus on the correlation between variables while ignoring negative noise from non-predictive variables and (ii) they are unable to distinguish the impact of chlorophyll from that of non-predictive variables on chlorophyll concentration at future time points. In order to overcome these obstacles, we propose a Multi-Attention Collaborative Network (MACN)-based triangle-structured prediction system. In particular, the MACN consists of two branch networks, with one named NP-net, focusing on non-predictive variables, and the other named T-net, applied to the target variable. NP-net incorporates variable-distillation attention to eliminate the negative effects of irrelevant variables, and its outputs are used as auxiliary information for T-net. T-net works on the target variable, and both its encoder and decoder are related to NP-net to use the output of NP-net for assistance in learning and prediction. Two actual datasets are used in the experiments, which show that the MACN performs better than various kinds of state-of-the-art techniques.