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wildfire-smoke-forecasting

topic1 events
papersSEP 10 04:00 UTC

Real-Time Training of Wildfire-to-Smoke Maps Enabled by Multilinear Operators

A new arXiv paper presents multilinear operator methods that make it practical to train a machine-learning model translating wildfire conditions into smoke forecasts in real time. Wildfire smoke is a significant source of fine particulate pollution, threatening public health and power grid reliability, and long-range prediction must also factor in fuel management choices and natural fuel evolution. The work aims to speed up training so smoke-impact models can support operational forecasting.

wildfire/climate/efficiency/research