Risk Identification and Prioritization in Hospital Construction Projects in Disaster-Prone Areas: A Contractor-Based Probability–Impact Matrix Approach

Oil spill pollution is a major threat to marine ecosystems, coastal economies, and global sustainability, with long-term ecological, economic, and health impacts arising from tanker accidents, offshore drilling, and industrial discharges. Traditional monitoring methods, such as satellite remote sensing, aircraft surveillance, and fixed detection systems, are limited by high cost, slow response, weather dependence, and restricted spatial coverage, motivating the need for autonomous, real-time solutions. This work proposes the design and MATLAB/Simulink-based simulation of an energy-efficient aquatic robot, driven by DC motors and controlled via microcontrollers (e.g., Arduino or Raspberry Pi), for direct oil spill detection on water surfaces. The robot integrates infrared sensors to exploit oil–water reflectance differences, volatile hydrocarbon gas sensors for vapor detection, and a digital camera for real-time imaging, with data processed via image processing, feature extraction, and machine learning algorithms to enhance detection accuracy and reduce false positives. MATLAB/Simulink is used to model the robot’s navigation, sensing, and decision-making in diverse sea conditions, enabling systematic testing of sensor response, data fusion, and robustness under varying oil contamination levels. Performance metrics such as detection accuracy, response time, energy consumption, and resilience to environmental noise are evaluated to yield a cost-effective, scalable, and autonomous platform capable of early warning alerts. The study contributes to advancing marine environmental monitoring technologies, improving rapid oil spill response strategies, and supporting sustainable ocean protection through robotics and simulation.