Role of Artificial Intelligence in Modernisation of Fire Risk Management

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.