Spatiotemporal prediction of chlorophyll-a in semi-enclosed gulfs using a hybrid graph neural network-transformer framework with satellite data and causal analysis.

Accurate monitoring and prediction of Chlorophyll-a (Chl-a) concentrations are critical for protecting desalination systems from algal blooms. This study presents an advanced framework employing the Graph Neural Network Transformer Model (GNN-T) to predict the spatiotemporal dynamics of Chl-a in semi-enclosed marine environments, such as the Persian Gulf, a key region for desalination facilities in the Middle East. The GNN-T model integrates critical environmental variables, utilizing MODIS/Aqua and ERA5 datasets with 300,000 observations. Demonstrating robust generalizability, the test model achieves an R² of 0.906 in the Gulf of Mexico, outperforming conventional deep learning approaches, including CNN-LSTM, BiLSTM, Temporal-Relational GNN, and AGTCNSD. Statistical error metrics confirm the GNN-T's superior predictive accuracy and lower error rates. Global sensitivity and uncertainty analysis (GSUA) highlights sea surface temperature, normalized fluorescence line height, and particulate organic carbon as key drivers. Convergent Cross-Mapping (CCM) elucidates nonlinear causal relationships, distinguishing correlation from mechanistic causality. Additionally, a causality-driven ablation study, guided by CCM and Sobol sensitivity analyses, streamlined the model by selecting the top 13 influential variables, achieving a test R² of 0.882 with 25% reduced computational costs, enhancing operational efficiency without substantial loss in predictive accuracy. Uncertainty quantification, performed using Monte Carlo dropout, provides 95% confidence intervals. Quartile analysis establishes bloom thresholds at the 50