Remote Sensing and GIS Applications in Environmental Sciences
Risk Vulnerability Modeling of Gas Distribution Network Pipelines Using the Mamdani Fuzzy Inference Approach and GIS (Case Study: Ilam City)
Document Type : Original Article
Authors
1 , Department of Remote Sensing and GIS, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran
2 Assistant Professor, Department of Remote Sensing and Geographic Information Systems, University of Tabriz
3 Department of Remote Sensing and GIS, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran
Abstract
The increasing reliance on natural gas transmission networks to meet both industrial and residential demands underscores the necessity of risk assessment and vulnerability analysis of these infrastructures against natural and human-induced hazards. This study aims to construct a spatial model for identifying and analyzing high-risk zones along gas transmission pipelines in Ilam City through the integration of Geographic Information Systems (GIS) and Mamdani fuzzy logic.
To achieve this, spatial data such as distance from riverbeds, landslide-prone areas, density of urban gas regulators, and pipeline diameter were collected. These datasets were transformed into information layers within the ArcGIS environment and normalized using fuzzy membership functions. A set of fuzzy inference rules based on the Mamdani approach was then formulated, and the inference process was executed to generate the final risk vulnerability map of the gas transmission pipelines.
The results revealed that areas situated near riverbeds, regions characterized by steep slopes and landslides, as well as zones with high regulator density and smaller pipeline diameters, exhibited the highest vulnerability. The produced maps demonstrated acceptable accuracy in identifying critical sections of the network, thereby providing valuable support for management strategies and decision-making processes concerning safety and maintenance.
Overall, the proposed model—by combining GIS spatial analysis with Mamdani fuzzy logic—offers a reliable and practical framework for detecting high-risk areas along urban gas transmission pipelines. Its application extends to safety planning, preventive risk management, and prioritization of maintenance operations, ultimately contributing to enhanced resilience and improved security of gas supply infrastructures.
Keywords
Main Subjects
)