Spatiotemporal Attention Network for Chl-a Prediction With Sparse Multifactor Observations

Abstract

Chlorophyll-a (Chl-a) is a critical indicator of water quality, and accurate Chl-a prediction is essential for marine ecosystem protection. However, existing methods for Chl-a prediction cannot adequately uncover the correlations between Chl-a and other environmental factors, e.g., sea surface temperature (SST) and photosynthetically active radiation (PAR). In addition, it is also difficult for these methods to learn the burst distributions of Chl-a data, i.e., increasing sharply for certain short periods of time and remaining stable for the rest of time. Furthermore, as original Chl-a, SST, and PAR data are often of high sparsity, most approaches rely on complete reanalysis data, which can incur accumulated error accumulation and degrade prediction performance. To address these three issues, we proposed a spatiotemporal attention network entitled SMO-STANet for Chl-a prediction. Concretely, the multibranch spatiotemporal embedding module and spatiotemporal attention module are developed to learn the correlations between Chl-a and the two external factors, i.e., SST and PAR, thus facilitating the learning of the underlying spatiotemporal distribution of Chl-a. In addition, we designed a scaled loss function to enable SMO-STANet to adapt to the burst distributions of Chl-a. Finally, we develop a sparse observation data completion module to address the issue of data sparsity. According to the experimental results on two real datasets, SMO-STANet outperforms existing methods for Chl-a prediction by a large margin. The code is available at https://github.com/ADMIS-TONGJI/SMO-STANet