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Selected risk factor analysis for coalbed methane development with optimized fuzzy based knowledge mapping at
Jianhua Zhang1, Md Helal Miah2, Muin Abdullah Al3
1School of Management, Zhengzhou University, Zhengzhou, Henan, China.
Scientific Reports
|December 16, 2025
Summary
This study introduces an advanced Fuzzy Cognitive Map (FCM) model with Interpretive Structural Modelling (ISM) to assess coalbed methane development risks. The Jamalganj case study reveals a medium social-ecological risk, highlighting pollutant concentrations, occupational hazards, and economic losses.
Area of Science:
- Environmental Science
- Risk Assessment
- Socio-economics
Background:
- Traditional coalbed methane development risk assessment is narrowly focused on economic factors.
- Significant gaps exist in comprehensively evaluating the social and environmental risks associated with coalbed methane extraction.
Purpose of the Study:
- To introduce an advanced framework for coalbed methane development risk assessment using Fuzzy Cognitive Map (FCM) integrated with Interpretive Structural Modelling (ISM) and triangular fuzzy numbers.
- To analyze the social-ecological risks of coalbed methane development in the Jamalganj region of Bangladesh.
Main Methods:
- Utilized Interpretive Structural Modelling (ISM) knowledge-mapping algorithm to analyze relationships between indicator nodes, constructing a comprehensive relationship matrix.
- Employed a Fuzzy Cognitive Map (FCM) model integrated with triangular fuzzy numbers and the nonlinear Hebbian learning algorithm for risk assessment.
- Conducted a case study in Jamalganj, Bangladesh, to exemplify the integrated modeling framework.
Main Results:
- The integrated FCM-ISM model determined a social-ecological risk value of 0.665 for coalbed methane development in Jamalganj, indicating a medium risk level.
- Identified distinct risk phases: early stages characterized by high pollutant concentrations, middle stages by mechanization and occupational hazards, and late stages dominated by economic loss.
- The nonlinear Hebbian learning algorithm refined the FCM model, enhancing the precision of environmental and socio-economic risk assessments.
Conclusions:
- The developed framework provides a robust method for assessing environmental and socio-economic risks in coalbed methane development.
- Findings highlight the need for phased management strategies to mitigate specific risks at different stages of development.
- The study aids policymakers in achieving sustainable resource management for coalbed methane projects.
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