Developing a Bayesian network model for environmental risks of the Caspian Sea breakwater in Bandar Anzali

Document Type : Original Article

Authors

1 Faculty Member/College of Engineering, University of Tehran

2 College of Environment,, Department of Environment

3 College of Environment, Department of Environment

10.48308/envs.2024.1389

Abstract

Efforts toward managing environmental risks of marine installations such as the Caspian Sea breakwaters can yield significant achievements in preventing and reducing associated risks and hazards. This research aims to model and analyze the environmental risks of the Caspian Sea breakwater in the Caspian Port. To this end, after identifying the activities and processes involved in the execution and operation phase, hazards and potentially damaging factors were identified. In the evaluation process, using the Failure Modes and Effects Analysis (FMEA) method, the intensity, probability of occurrence, and probability of detection of each risk were completed based on the opinions of experts and specialists. By calculating the risk score, critical risks were identified, with the highest priority coefficient in non-human-related risks being 384 and in human-related risks being 126. These priorities, along with the frequency of their occurrence, were entered into Netica software and Bayesian networks for critical risk analysis and modeling. The results of this analysis indicate that the highest human-related risks are related to skin damage with a quantitative value of 0.167, auditory impairments with a quantitative value of 0.004 directly, and soil pollution with a quantitative value of 0.125, and noise pollution with a quantitative value of 0.004 for indirect risks to humans. Furthermore, in the section on non-human-related risks, the highest risk is related to the use of hazardous substances with a quantitative value of 0.024, water pollution with a quantitative value of 0.224, and the depletion of resources resulting from mining with a quantitative value of 0.764. The interdependence between risks and their mutual effects in Bayesian analysis is clearly observable. A precise understanding of these dependencies and influential risks can address environmental safety challenges in the region by providing effective local solutions and lead to their reduction.




Efforts toward managing environmental risks of marine installations such as the Caspian Sea breakwaters can yield significant achievements in preventing and reducing associated risks and hazards. This research aims to model and analyze the environmental risks of the Caspian Sea breakwater in the Caspian Port. To this end, after identifying the activities and processes involved in the execution and operation phase, hazards and potentially damaging factors were identified. In the evaluation process, using the Failure Modes and Effects Analysis (FMEA) method, the intensity, probability of occurrence, and probability of detection of each risk were completed based on the opinions of experts and specialists. By calculating the risk score, critical risks were identified, with the highest priority coefficient in non-human-related risks being 384 and in human-related risks being 126. These priorities, along with the frequency of their occurrence, were entered into Netica software and Bayesian networks for critical risk analysis and modeling. The results of this analysis indicate that the highest human-related risks are related to skin damage with a quantitative value of 0.167, auditory impairments with a quantitative value of 0.004 directly, and soil pollution with a quantitative value of 0.125, and noise pollution with a quantitative value of 0.004 for indirect risks to humans. Furthermore, in the section on non-human-related risks, the highest risk is related to the use of hazardous substances with a quantitative value of 0.024, water pollution with a quantitative value of 0.224, and the depletion of resources resulting from mining with a quantitative value of 0.764. The interdependence between risks and their mutual effects in Bayesian analysis is clearly observable. A precise understanding of these dependencies and influential risks can address environmental safety challenges in the region by providing effective local solutions and lead to their reduction.

Keywords