Smart City Wildfire Risk Analysis with Fuzzy Multi-Criteria Decision-Making

Uncontrolled wildfires pose a significant threat, potentially causing extensive damage to biodiversity, soil quality and human resources. It's crucial to swiftly detect and predict these wildfires to minimize their catastrophic consequences. To address this, our research introduces a wildfire prediction model that ranks cities based on risk leveraging multi-criteria decision-making (MCDM) to systematically assess conflicting factors in decision-making. This model integrates wildfire risks into a city's resilience strategy, utilizing fuzzy set theory to manage imprecise data and uncertainties. As part of this approach, we compile a new dataset encompassing weather patterns, vegetation types, terrain features and population density across various Californian cities. Ultimately, the model assesses and ranks the wildfire risk for each city in California.