Hassan Akbari

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^th (bloom), 75^th (intense bloom), and 90^th percentiles (extreme bloom) for probabilistic risk assessments. This model serves as an effective operational tool for detecting algal bloom onset and mitigating associated economic impacts.

The performance of seawater intake systems affects the reliability and efficiency of desalination plants and water-processing systems. The objective of this work is to gain insights into improving their design by examining the flow patterns around seawater intakes using particle image velocimetry (PIV), image processing techniques, and numerical modeling. Different wave and current conditions are considered, and intake conditions are classified into categories based on hydrodynamic parameters. Numerical simulations indicate complex flow patterns under simultaneous waves and currents. The results revealed that the velocity of the approach current affects the efficiency of seawater intake, and the impact depends on the cap geometry. Square caps, characterized by sharp edges, create flow contractions and instabilities, whereas circular caps result in smoother flow patterns, enhancing efficiency. Wave action exacerbates these effects, particularly as the Keulegan–Carpenter (KC) number increases, and may compromise the stability of intake structures. Circular caps improve overall stability and performance under waves. These results contribute to better designs of seawater intake structures and, thus, improved efficiency and stability.

Resilient and sustainable port infrastructures are vital in addressing the growing complexities and uncertainties of modern maritime systems. This study emphasizes the necessity of an integrated understanding of four interrelated concepts (risk, resilience, reliability, and sustainability) in the context of port planning and operations. Despite the abundance of research on ...