Multi-Scale Spatiotemporal Graph ODE Networks for Marine Chlorophyll-a Prediction

Chlorophyll-a concentration is a key indicator reflecting the growth status of phytoplankton, and its accurate prediction is of great significance for assessing the degree of water eutrophication. Although existing approaches have achieved good performance, they generally pay insufficient attention to multi-scale spatial information and show limitations in characterizing the continuous spatiotemporal dynamics. To address these issues, this paper proposes a multi-scale spatiotemporal graph ODE network (MGODE) for ocean chlorophyll-a prediction. The MGODE adopts a dual-layer structure, simultaneously processing chlorophyll-a concentration data at both the region level and node level to capture multi-scale spatial features, and it enables effective interaction of cross-scale features through dynamic transmission coefficients and a gated fusion mechanism. Meanwhile, the MGODE employs a dual-ODE architecture at both the node and region levels, utilizing spatiotemporal ODE blocks to continuously and deeply capture features, thereby simulating the continuous spatiotemporal dynamic evolution of chlorophyll-a. Experiments on real-world datasets from the Bohai Sea and South China Sea show that the proposed MGODE model achieves higher prediction accuracy than several current state-of-the-art models. Compared with the best baseline, the MGODE achieves reductions of 2.78% in MAE and 1.07% in RMSE on the Bohai Sea dataset and reductions of 1.19% in MAE and 1.38% in RMSE on the South China Sea dataset. These results demonstrate the potential of the MGODE to support marine chlorophyll-a forecasting and marine ecological monitoring.