Multistep-Ahead Forecasting of Chlorophyll Concentration Based on Dynamic Collaborative Attention Network

Abstract

Multistep-ahead forecasting of chlorophyll concentration is of great significance in red tide early warning systems. Existing methods often neglect the potential adverse interactions between non-predictive variables and chlorophyll while failing to fully utilize the effective information in historical decoder units. To address these issues, this paper proposes a Dynamic Collaborative Attention Network (DCAN) model for chlorophyll concentration forecasting, which consists of two components: a Two-Stage Variable Embedding Network (TSVEN) and a Dynamic Attention Network (DyAN). The TSVEN can identify the non-predictive variables that have the most significant impact on chlorophyll changes and generate corresponding spatial vectors from them, thereby alleviating the information conflict between chlorophyll and non-predictive variables. The DyAN integrates a context attention module and a filtering gate mechanism. The former effectively extends the forecasting time range by dynamically retrieving historical decoder states, while the latter selectively integrates historical decoder information, thereby improving the reliability of model decisions and prediction accuracy. Experimental results based on real datasets show that the proposed model outperforms the current state-of-the-art methods in chlorophyll concentration forecasting tasks and exhibits good interpretability.