Spatial-temporal attention network for multistep-ahead forecasting of chlorophyll

Abstract

The multistep-ahead prediction of chlorophyll provides an effective means for early warning of red tide. However, since multistep-ahead forecasting presents challenges, such as vague interactive relationships among ocean factors, long-term dependence modeling, and accumulative errors, existing methods mostly concentrate on the current time or one-step-ahead forecasting. In this paper, a hierarchical multistep-ahead forecasting model spatial-temporal attention network(STAN), which integrates the spatial context extractor network(SCE-net), long short-term memory network(LSTM), and the temporal attention mechanism, is proposed for the prediction of chlorophyll. In STAN, the input layer utilizes SCE-net to excavate relationships among ocean factors and generate high-level semantic via embedding factors into a continuous low-dimensional space. The middle layer applies LSTM to build the long-term dependencies of corresponding semantic representations. The output layer uses another LSTM with temporal attention to reduce accumulative errors and maintain temporal continuity. The attention can assign different weights to the middle layer’s hidden state and generate a context vector. Then the context vector and the final predicted value are considered as the current input for better forecasting. The buoy observation data of the Xiamen coastal area monitored in 2009–2011 is used to verify the efficiency of STAN. Experimental results prove that STAN outperforms the state-of-the-art methods of multistep-ahead prediction. When using 7 observation steps to forecast 15 steps, the MAPE of STAN is 0.3209, and the MAE is 0.1 lower than the values of the baselines approaches.