Selected risk factor analysis for coalbed methane development with optimized fuzzy based knowledge mapping at Jamalganj coalfield in Bangladesh

This research addresses significant gaps in coalbed methane development risk assessment, traditionally focused narrowly on economic aspects. Introducing an advanced "Fuzzy Cognitive Map" (FCM) model integrated with "Interpretive Structural Modelling" (ISM) and triangular fuzzy numbers, this study offers a robust framework, exemplified in Jamalganj case study in Bangladesh. The study uses the ISM knowledge-mapping algorithm to analyze relationships between indicator nodes. Starting with a direct relationship matrix, the algorithm constructs a comprehensive relationship matrix that includes both direct and indirect connections. This involves identifying direct influences between nodes and their permutations in the transposed matrix. The algorithm's iterative process ensures a thorough mapping of interactions, resulting in a nuanced understanding of the system's structure and dynamics. This method enhances the precision of relationship modeling and risk assessment. The fuzzy cognitive model analysis reveals that the risk value for coalbed methane development in Jamalganj is 0.665, indicating a medium level of social ecological risk. Early stages show high pollutant concentration values, while the middle stage highlights production mechanization and occupational hazards. In the late stage, economic loss becomes the dominant factor, requiring focused management to mitigate its impact. Integrating ISM knowledge mapping and triangular fuzzy numbers into the FCM model, refined by the nonlinear Hebbian learning algorithm, enhances environmental and socio-economic risk assessments in coalbed methane development, aiding policymakers in sustainable resource management.