Order-Based Risk and Machine Learning Framework for Wildfire Detection and Monitoring

There is urgency to address the planetary emergency of wildfires. With severe environmental, economic, health, and societal impacts, wildfires threaten ecosystems and communities. Early detection and effective monitoring are vital for improving response efforts and reducing the impacts of wildfires. The development of a vast array of detection and monitoring technologies calls for a comprehensive analysis approach that provides insights into detection and monitoring capabilities while recognizing the complex context in which these technologies operate. This work proposes a framework that integrates systems and lifecycle analysis, scenario-based disruptions of system order, and machine learning classification techniques for analysis of detection and monitoring technologies and systems. The framework is demonstrated for wildfire detection and monitoring. Systems and lifecycle analysis identifies sixteen categories of wildfire detection and monitoring technologies and examines their strengths and weaknesses throughout the phases of a wildfire. Order-based risk analysis determines the prioritization of technologies under a baseline scenario and investigates the changes in priority order under six potential future scenarios that encompass changes in environmental and societal factors, providing insights into the impacts of future disruption. Based on the results of the systems and lifecycle analysis and order-based risk analysis, multiple machine learning smoke detection models are developed for integrated systems of detection and monitoring technologies to provide insights into how technologies can be leveraged together to improve detection capabilities, including accuracy, precision, and time to detection.