AI DRIVEN FIRE RISK INDICES INTEGRATING CLIMATE, FUEL, AND TERRAIN FOR WILDFIRE PREDICTION AND MANAGEMENT

Wildfires pose a growing global threat, exacerbated by climate change, deforestation, and urban expansion into
fire-prone regions. Traditional fire risk assessment methods often rely on static indices that fail to capture the
dynamic interactions between climate conditions, fuel availability, and terrain characteristics. Artificial
intelligence (AI) offers a transformative approach to wildfire prediction and management by integrating multisource environmental data into adaptive fire risk indices. This study explores the development of AI-driven fire
risk indices that leverage machine learning (ML) and deep learning (DL) algorithms to analyze complex patterns
in climate variability, vegetation moisture, and topographical features. By integrating remote sensing data, weather
forecasts, and historical fire occurrences, AI models can enhance the accuracy of fire risk assessments and enable
proactive mitigation strategies. The research highlights the role of geospatial AI and predictive analytics in
mapping high-risk zones and optimizing resource allocation for firefighting efforts. Furthermore, the application
of explainable AI (XAI) ensures transparency in decision-making, fostering trust among emergency responders
and policymakers. Case studies from wildfire-prone regions illustrate the efficacy of AI-based risk indices in early
warning systems and real-time fire spread modeling. However, challenges such as data sparsity, model
interpretability, and computational constraints must be addressed for broader implementation. This paper
underscores the need for interdisciplinary collaboration between AI researchers, climate scientists, and disaster
management agencies to enhance wildfire prediction and mitigation strategies. Future research should explore
hybrid AI models integrating reinforcement learning and edge computing for real-time risk assessment in rapidly
changing wildfire conditions.