Machine learning and artificial intelligence for wildfire prediction

Technical Report · Thu Apr 15 04:00:00 EDT 2021

DOI:https://doi.org/10.2172/1769739· OSTI ID:1769739

Randerson, James T. ^[1]; Georgiou, Efi Foufoula ^[1]; Smyth, Padhraic ^[1]; Chen, Yang ^[1]; Coffield, Shane ^[1]; Graff, Casey ^[1]; Hantson, Stijn ^[1]; Goulden, Michael ^[1]; Hall, Alex ^[2]; Kueppers, Lara ^[3]; Riley, William ^[4]; Zhu, Qing ^[4]; Dubey, Manvendra ^[5]; Linn, Rodman ^[5]; Tang, Qi ^[6]; Mao, Jiafu ^[7]; Morton, Douglas ^[8]

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1. Univ. of California, Irvine, CA (United States)
2. Univ. of California, Los Angeles, CA (United States)
3. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
5. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
6. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
7. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
8. NASA Goddard Inst. for Space Studies (GISS), New York, NY (United States)

Wildfire ignition, intensity, and spread rates are tightly linked with water cycle extremes. The science of wildfire prediction has traditionally encompassed the use of physical and empirical models to quantify the direction and speed of fire spread, plume injection and fire-aerosol impacts on atmospheric composition, predictions of fire season severity on subseasonal-to-seasonal (S2S) time scales, and assessment of the spatial and temporal patterns of fire risk across landscapes. Together with expanding observation networks, machine learning and artificial intelligence (AI) have the potential to revolutionize the application of such models for fire science, saving lives, protecting critical infrastructure, and providing more accurate estimates of wildfire-climate feedbacks.

• Research Organization: Univ. of California, Irvine, CA (United States); Univ. of California, Los Angeles, CA (United States); Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); NASA Goddard Inst. for Space Studies (GISS), New York, NY (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• OSTI ID: 1769739
• Report Number(s): AI4ESP1109
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
58 GEOSCIENCES
97 MATHEMATICS AND COMPUTING