Enhancing wildfire risk assessment through weather modeling and machine learning: a case study in Orange County, USA

Abstract

Forest wildfire is a significant threat, causing extensive environmental damage and loss of life. Accurate and effective risk assessments are essential for better understanding and mitigating this risk. In this study, we propose a novel approach that combines weather modeling and machine learning for improved wildfire risk assessment. Our case study focuses on Orange County, USA, known for its high wildfire susceptibility. The methodology involves collecting historical weather and wildfire data in the region. We employ a machine learning model that integrates weather data with other factors, including vegetation and topography, to predict wildfire risk. Through machine learning techniques, we analyze the relationships among these variables and generate accurate risk predictions for Orange County. Results indicate a significant enhancement in wildfire risk assessment with this approach. The developed model provides valuable insights into the factors influencing wildfires, aiding authorities, and policymakers in implementing effective mitigation strategies. By leveraging weather modeling and machine learning, we can enhance our understanding and management of wildfire risks, safeguarding the environment and communities. This research highlights the potential of these technologies to improve wildfire mitigation efforts. The findings contribute to a proactive approach to addressing the serious threat of forest wildfires.