جستجوی مقالات مرتبط با کلیدواژه "bayesian networks" در نشریات گروه "محیط زیست"

Managing environmental risks associated with marine installations, such as the breakwaters of the Caspian Sea, plays a critical role in mitigating potential hazards and ensuring sustainable development. The Caspian Sea, a unique and environmentally sensitive region, faces significant ecological risks due to construction and operational activities related to breakwaters. This study aims to model and analyze the environmental risks specifically related to the breakwater located in the Caspian Port. By comprehensively identifying the various activities and processes during both the construction and operation phases, this research seeks to uncover potential hazards and damaging factors. The ultimate objective is to provide a framework for preventing or minimizing these risks, thus contributing to the long-term environmental sustainability of the region.

In this research, the Failure Modes and Effects Analysis (FMEA) method was employed to evaluate the environmental risks. FMEA is a widely used risk assessment tool that helps in determining the severity, likelihood of occurrence, and detectability of risks. Expert opinions were collected to assess these factors for each identified risk. Following this evaluation, the risk priority number (RPN) was calculated, which helped identify the critical risks requiring immediate attention. The highest RPN for non-human-related risks was 384, while for human-related risks, it was 126. These priority levels were further analyzed using Bayesian networks through the Netica software, a tool known for efficiently modeling risk interdependencies.

The analysis of human-related risks revealed that skin damage posed the highest risk, with a quantitative value of 0.167. Direct auditory impairments were less significant, with a value of 0.004, while indirect human risks included soil pollution (0.125) and noise pollution (0.004). These findings indicate that while direct physical harm to individuals may not be highly prevalent, indirect risks, especially related to environmental degradation, hold substantial importance. On the other hand, in the category of non-human-related risks, the most critical hazard was identified as the depletion of natural resources due to mining activities, with a high quantitative value of 0.764. Water pollution (0.224) and the use of hazardous substances (0.024) were also identified as key risks impacting the environment. The Bayesian network analysis effectively highlighted the interconnections between these risks, revealing how the occurrence of one risk could amplify others, demonstrating a web of interdependent risk factors.

The results underscore the significance of understanding the interdependence of risks when addressing environmental challenges in marine construction projects. The use of Bayesian networks in this study clearly demonstrated the mutual influence between different risk factors, emphasizing the need for an integrated risk management approach. By identifying critical risks and understanding their interdependencies, decision-makers can implement targeted and localized solutions to mitigate these risks.