With the rapid development of port construction and the shipping industry, port water quality issues are of great concern. This is always a challenging task due to the frequent human activities and dynamic processes involved. A parallel intelligent water quality monitoring system is therefore proposed to ensure the effective monitoring and intelligent control of water pollutants. The real monitoring system and the artificial monitoring system of port water quality are established by applying artificial systems, computational experiments and parallel execution (ACP method). Both systems interact with each other and execute in parallel. The artificial monitoring system simulates complex scenarios, while the real monitoring system feeds the artificial monitoring system with actual monitoring data. By means of data-driven and model-driven approaches, the two systems can compute, observe and evaluate to control, manage and train models. Through the continuous optimization between the two systems, the efficiency and accuracy of the water quality monitoring system could be improved. Technical support can be further provided for the planning of water quality monitoring sites, implementation of monitoring tasks, allocation of emergency resources, etc. As in-situ monitoring data are obtained, computational experiments and parallel executions could be conducted to achieve the ultimate goal of port water quality management.

With the rapid development of the marine shipping industry and green ports, the risk of water pollution caused by vessel traffic accidents has attracted widespread concern. Port water pollution risk assessment can provide guidance for green port construction and the optimal allocation of emergency resources. This study develops a structured risk assessment framework that integrates a Bayesian network (BN), the Analytic Hierarchy Process (AHP), and a fuzzy risk matrix. The BN is used to estimate accident probability under uncertainty, the AHP is used to quantify multidimensional accident consequences, and the fuzzy risk matrix incorporates uncertainty and decision-makers’ risk preferences into the final risk evaluation. Furthermore, the improved fuzzy risk matrix addresses uncertainty in port water pollution risk assessment. A case study was conducted at Zhanjiang Port to validate the capability of the model. The results indicate that the probability of a ship accident is approximately 0.0528 and that the accident consequence index is 3.304. The comprehensive risk indices are 3.27 (Easy), 3.72 (Standard), and 3.99 (Hard) for the different risk matrices. These results fall within the Tolerable Unacceptable or Unacceptable risk levels. The comprehensive risk indices are obtained using three fuzzy risk matrices with different risk preferences and costs. The proposed framework enhances the transparency and practical applicability of port water pollution risk assessment and provides structured decision support for marine environmental management.