AI and Simulation Enhance Aerial Wildfire Suppression Precision
A new machine-learning system combines real-time sensor data with advanced simulations to optimize aerial firefighting drops, improving efficiency and safety.
Wildfires are increasing in frequency and intensity globally, exacerbated by climate change. In response, researchers at the Fraunhofer Institute for Industrial Mathematics ITWM and startup CAURUS Technologies GmbH are developing a machine-learning system to enhance the precision of aerial firefighting. This system aims to determine the fire situation in real-time and calculate optimal times for water drops.
The collaborative effort integrates CAURUS Technologies' mobile sensor platform with a prediction system and Fraunhofer ITWM's MESHFREE simulation software. This combination allows for real-time prediction and optimization of aerial drops, leading to a learning system designed to assist emergency responders in planning, executing, and evaluating operations more effectively. The system promises to improve safety for helicopter crews by enabling more precise, situation-specific targeting from higher altitudes.
Currently, the effectiveness of aerial water drops relies heavily on the experience of helicopter crews, with factors like wind and forest type significantly influencing outcomes. Drops are typically made from low altitudes (15-40 meters), and environmental conditions can affect suppressant distribution. Small changes in drop parameters, such as altitude and timing, can improve effectiveness by over 20%, highlighting the value of immediate feedback.
The CAURUS sensor platform, mounted above the water bucket, uses HD and infrared cameras along with position sensors to gather real-time data on the fire. This georeferenced data provides a clear overview of the fire situation. This data, in turn, is used within the Forest Shield project to generate real-time predictions for suppressant drops, allowing response management to assess the impact of each drop.
To train the prediction systems for complex aerial drops, extensive datasets are required. The MESHFREE simulation software plays a crucial role by using sensor and image data from the CAURUS platform to create a digital twin of the drop area. This enables precise simulation and analysis of water drops, incorporating climatic factors like wind and forest structure. Machine-learning surrogate models, trained on this simulation data, can then provide rapid, on-site predictions by approximating complex simulations.
This development represents a significant advancement in using digital tools for emergency response. By integrating real-time sensor data with sophisticated physics-based simulations and machine learning, the system moves beyond static campaign planning to dynamic, in-situ decision support. This mirrors trends in other fields like aerospace, where simulations and sensor fusion are critical for mission success and operational efficiency, potentially offering parallels for future in-situ resource utilization or construction applications.
Edited by the news editor with AI from the original report — please refer to the original source.