Abstract
Fire in the form of wildfire, indoor fire, and bombardment, regardless of their natural or manmade origin, impacts substantially the economic as well as environmental hazards such as Air Pollution. This research aims to identify the role of artificial intelligence (AI) in modernising fire risk management. Using interpretive structural modeling (ISM) techniques, we can understand the interdependencies and hierarchical relationships within this context. AI enables the analysis of vast amounts of data from various sources, including historical fire incidents, weather patterns, building structures, and human behaviour, to assess and predict fire risks more accurately. ISM is a computational technique that uses a qualitative and interpretive approach to address intricate issues by mapping the relationships between variables and converting them into a multilevel structural model. Interpretive Structural Modeling (ISM) is a mathematical and qualitative tool used to identify key variables and create a hierarchical model that illustrates their interrelationships. Seven variables have been identified based on literature and expert input. Variables have been classified based on their influence and reliance.
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