Wildfire Monitor: Real-Time Prediction and Interpretable Alerts from Fused Satellite and Meteorological Data - Souza, Luís Fabrício de Freitas; Severiano, Guilherme F. B.; Nascimento, José Jerovane da C.; Fernandes, Pedro Hugo Ursulino; Rodrigues, Ícaro de Sousa; Silva, Jesus Ossian Cunha; Silva, Francisco Italo G. da; Landim Junior, Osvaldo Soares (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI); 2026: Anais do XXII Simpósio Brasileiro de Sistemas de InformaçãoDO - 10.5753/sbsi.2026.248736, 2026)
Research Context: Wildfires cause significant environmental, economic, and social damage, requiring intelligent systems capable of supporting prevention and mitigation actions. Scientific and/or Practical Problem: The increasing frequency of wildfires in Brazil and worldwide demands innovative technological approaches for real-time monitoring and accurate prediction of fire outbreaks using reliable and official data sources. Proposed Solution and/or Analysis: This paper presents the Cariri Wildfire Monitor, a system for mapping and monitoring wildfires through a web portal that integrates Artificial Intelligence (AI) and official datasets. The proposal includes a technological solution for continuous monitoring and a predictive module for fire hotspots. Related IS Theory: The system design is grounded in Information Systems (IS) theories related to data-driven decision support, predictive analytics, and socio-environmental information integration for sustainable management. Research Method: The system performs data fusion from INPE and INMET sources, applies regression models to predict wildfire hotspots, and integrates a decision layer with Large Language Models (LLMs) to produce auditable alerts and qualitative insights. Summary of Results: Among the results, the ExtraTrees regressor achieved R 2 =0,9843. In the stratified audit of the LLM module with live threshold 0,758 (proxy 0,500; N pop =1000; M audit used = 12), we obtained Accuracy (w) 0,7973±0,1165, and p95 latencies between 4,75s and 5,95s. Contributions and Impact to IS area: The system demonstrates the potential of predictive computational models and LLMs in environmental monitoring, contributing to the development of intelligent, auditable, and sustainable Information Systems solutions for wildfire management.