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Abstract The increasing size and severity of wildfires across the western United States have generated dangerous levels of PM2.5concentrations in recent years. In a changing climate, expanding the use of prescribed fires is widely considered to be the most robust fire mitigation strategy. However, reliably forecasting the potential air quality impact from prescribed fires, which is critical in planning the prescribed fires’ location and time, at hourly to daily time scales remains a challenging problem. In this paper, we introduce a spatio-temporal graph neural network (GNN)-based forecasting model for hourly PM2.5predictions across California. Utilizing a two-step approach, we use our forecasting model to predict the net and ambient PM2.5concentrations, which are used to estimate wildfire contributions. Integrating the GNN-based PM2.5forecasting model with simulations of historically prescribed fires, we propose a novel framework to forecast their air quality impact. This framework determines that March is the optimal month for implementing prescribed fires in California and quantifies the potential air quality trade-offs involved in conducting more prescribed fires outside the peak of the fire season.
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A micro-UAS to Start Prescribed Fires
Prescribed fires have many benefits, but existing ignition methods are dangerous, costly, or inefficient. This paper presents the design and evaluation of a micro-UAS that can start a prescribed fire from the air, while being operated from a safe distance and without the costs associated with aerial ignition from a manned aircraft. We evaluate the performance of the system in extensive controlled tests indoors. We verify the capabilities of the system to perform interior ignitions, a normally dangerous task, through the ignition of two prescribed fires alongside wildland firefighters
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- Award ID(s):
- 1638099
- PAR ID:
- 10026209
- Date Published:
- Journal Name:
- International Symposium on Experimental Robotics
- Volume:
- 1
- Issue:
- 1
- Page Range / eLocation ID:
- 12-24
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-319-50115-4_2
