The Design and Development of a Machine Leaning Wildfire UAV Swarm Algorithm: IPCA

With the frequency of wildfires increasing, specifically across the Western United States, there exists a need for the reduction in damage to both the community and environment. Many urbanized areas and communities have been significantly affected or demolished by the rapid and devastating spread of recent wildfires. As the impact of global warming increases and the weather patterns begin to vary, the ability to contain a wildfire quickly begins to pose a challenge. While current solutions exist on the market, they are simply not designed for the increasingly faster spread because of ever- changing weather patterns. Additionally, the need for firefighters to have specialized experience has caused a rift between the number of those who can contain fires and the number of fires to be contained. The purpose of this project is the development of a machine learning wildfire UAV swam algorithm to allow the rapid and real time update of containment line calculations/visualizations to allow first- responders to effectively and quickly contain wildfires. By measuring major heat signatures, the UAV system can identify wildfires, and through TensorFlow analysis of wind patterns and environmental markers, make feedback decisions of where to search for spot fires. The combination of wildfires and the spot fires that may arise, result in a containment line visualization with coordinates for first responders to utilize. The goal of the IPCA is a functional autonomous system that can provide the prediction and aid for first responders to have another line of defense against the spread of wildfires into residential and urbanized areas.