AI-Driven Wildfire Management: An Integrated Approach to Detection, Prevention, and Response

Abstract

This comprehensive article explores the integration of artificial intelligence in wildfire management systems, presenting an innovative approach to detection, prevention, and response strategies. The article examines how AI technologies transform traditional firefighting methods through advanced sensing, predictive analytics, and resource optimization. The article encompasses various aspects of modern wildfire management, including drone-based surveillance, IoT sensor networks, and machine learning algorithms for fire behavior prediction. The article analyzes the implementation of automated risk assessment systems, public safety protocols, and emergency response coordination mechanisms. Through detailed case studies of existing deployments like the Cerberus system, CAL FIRE AI system, and IBM Watson platform, the research demonstrates significant improvements in detection accuracy, response times, and resource efficiency. The article also addresses integration challenges, policy considerations, and future development opportunities in AI-driven wildfire management systems. By examining both technological and operational aspects, this article provides valuable insights into the evolving landscape of wildfire management and establishes a framework for future implementations across diverse geographical regions.