Heterogeneity-Aware Multi-Step Chlorophyll-a Forecasting for Marine Water Quality Monitoring Using a Multi-Scale Spatio-Temporal Mixture-of-Experts Network

Chlorophyll-a concentration (Chl-a) is a key indicator of marine water quality, phytoplankton biomass, and aquatic ecosystem status. Multi-step forecasting is challenging because Chl-a dynamics exhibit regional heterogeneity, multi-scale variability, and complex spatial dependence. Existing models often optimize domain-averaged errors, which can mask unstable node-level predictions at high-variability nodes. We propose MS-STMoE, a heterogeneity-aware multi-scale spatio-temporal mixture-of-experts framework. It uses a Haversine-distance-based K-nearest-neighbor graph, gated multi-scale temporal convolutions with seasonal encoding to model short-term and periodic variations, and node-level sparse top-k routing that assigns differentiated expert pathways to nodes with distinct dynamics based on recent-state, temporal-mean, temporal-change, and node-prior features. Using 30-day histories to forecast the next 15 days, experiments on 300 Bohai Sea and 265 South China Sea nodes show that MS-STMoE achieves the lowest average MAE and RMSE among six recent spatio-temporal baselines. Compared with the best baseline, MAE and RMSE decrease by 5.6% and 2.7%, respectively, in the Bohai Sea, and by 11.0% and 5.0%, respectively, in the South China Sea. Step-wise and node-wise analyses indicate improved medium-to-late-horizon accuracy and modest, region-dependent reductions in high-error node tails, supporting more reliable region-aware short- to medium-term water quality monitoring.