Advanced Wildfire Planning and Response, Introducing Artificial Intuition/Quantitative Complexity Management (QCM)

The increased activity of wildfires and wildland fires has elevated the importance of early detection to manage fires or to greatly reduce the impacts. This has increased the need for rapid, accurate computational methods in evaluating the pre-fire, active fire, and post-fire environment. The QCM/Artificial Intuition approach is an innovative model free algorithm that acts similar to the human brain to recognize locations/areas of change and environmental stressors. Through QCM, outputs of forest system sensitivity and stress can be generated within minutes of receiving NASA remotely sensed data, and other sensor data, and identify areas that are prone to wildfires or wildland fires, help schedule prescribed burns, or track fire progression. This approach is more advantageous than typical Artificial Intelligence/Machine Learning (AI/ML) techniques as it requires less data to produce useful results and does not require training. For example the QCM/AI approach allows forest and grassland personnel time to plan prescribed burns, anticipate burn potential, and even support post fire burned area emergency response (BAER) teams to plan and respond. It can also be used for forest historical state of health monitoring and prognostics; detection and evaluation of insect infestation events; short- and long-term impacts of drought and fire events and quantitative ranking of overall forest resiliency. Used in combination with traditional artificial intelligence it is possible to provide early analysis. The aforementioned outputs can be displayed in real-time through GeoCollaborate, a cloud-based, cross-platform technology, which can rapidly share disparate and trusted data, in a real-time collaborative environment, to any user in the field and incident command centers. SAIC is currently employing the QCM/Artificial Intuition approach for several technology development programs for the DoD, DHS, FAA and other government agencies. The application can be integrated with other indicators, tools and applications at the Federal, Regional, State or local level. Details of the Artificial Intuition process will be explained and case studies from recent wildfires will be showcased that highlight this data analysis capability.