A Rigorous Comparison of Various Machine Learning Models for Next-Day Wildfire Detection
DOI:
https://doi.org/10.47611/jsrhs.v14i1.8856Keywords:
machine learning, wildfire, prediction, next-day, wildfire prediction, wildfire detection, AI, artificial intelligenceAbstract
Wildfires in the western United States pose significant threats to large, barren areas, as well as more populated, urban lands, especially in wildfire-prone areas in the West. This study aims to develop an Artificial Intelligence model that predicts wildfires on a next-day basis, enabling timely mitigation and safety efforts, which is beneficial, especially in high-risk areas like the Western United States. Understand how safety and health-related measures can be implemented by predicting how the wildfire will spread over a day. Environmental factors such as elevation, maximum and minimum temperatures, wind speed, humidity, precipitation, drought indices, vegetation health metrics (Normalized Difference Vegetation Index), population density, energy release, and initial fire masks (spreads) are features used by the model to make predictions about the spread of wildfires over 24-hours. The model's performance was evaluated based on its precision and recall in predicting fire spread within a 64 km x 64 km area. The results demonstrate the neural network’s strong capability to identify high-risk areas for wildfires with an 84% accuracy, improved by boosting with a Random Forest Classifier to 99% accuracy. By providing reliable next-day predictions, this model serves as a valuable tool for wildfire management, enabling authorities to implement preemptive measures that could significantly reduce the impact of wildfires. This research and model development contributes to the broader field of disaster prevention and management, offering a data-driven approach to mitigating wildfires on a next-day basis.
Downloads
References or Bibliography
Burke, M., Driscoll, A., Heft-Neal, S., Xue, J., Burney, J., & Wara, M. (2021). The changing risk and burden of wildfire in the United States. Proceedings of the National Academy of Sciences of the United States of America, 118(2), e2011048118. https://doi.org/10.1073/pnas.2011048118
Aguilera, R., Luo, N., Basu, R., Wu, J., Clemesha, R., Gershunov, A., & Benmarhnia, T. (2023). A novel ensemble-based statistical approach to estimate daily wildfire-specific PM2.5 in California (2006-2020). Environment international, 171, 107719. https://doi.org/10.1016/j.envint.2022.107719
Sharma, S., Chandra, M. & Kota, S.H. Health Effects Associated with PM2.5: a Systematic Review. Curr Pollution Rep 6, 345–367 (2020). https://doi.org/10.1007/s40726-020-00155-3
Yu, A. C., Lopez Hernandez, H., Kim, A. H., Stapleton, L. M., Brand, R. J., Mellor, E. T., Bauer, C. P., McCurdy, G. D., Wolff, A. J., 3rd, Chan, D., Criddle, C. S., Acosta, J. D., & Appel, E. A. (2019). Wildfire prevention through prophylactic treatment of high-risk landscapes using viscoelastic retardant fluids. Proceedings of the National Academy of Sciences of the United States of America, 116(42), 20820–20827. https://doi.org/10.1073/pnas.1907855116
Pope, C.A., Ezzati, M., Cannon, J.B. et al. Mortality risk and PM2.5 air pollution in the USA: an analysis of a national prospective cohort. Air Qual Atmos Health 11, 245–252 (2018). https://doi.org/10.1007/s11869-017-0535-3
Liu, J. C., Wilson, A., Mickley, L. J., Dominici, F., Ebisu, K., Wang, Y., Sulprizio, M. P., Peng, R. D., Yue, X., Son, J. Y., Anderson, G. B., & Bell, M. L. (2017). Wildfire-specific Fine Particulate Matter and Risk of Hospital Admissions in Urban and Rural Counties. Epidemiology (Cambridge, Mass.), 28(1), 77–85. https://doi.org/10.1097/EDE.0000000000000556
McClure, C. D., & Jaffe, D. A. (2018). US particulate matter air quality improves except in wildfire-prone areas. Proceedings of the National Academy of Sciences of the United States of America, 115(31), 7901–7906. https://doi.org/10.1073/pnas.1804353115
Radeloff, V.C., Hammer, R.B., Stewart, S.I., Fried, J.S., Holcomb, S.S. and McKeefry, J.F. (2005), THE WILDLAND–URBAN INTERFACE IN THE UNITED STATES. Ecological Applications, 15: 799-805. https://doi.org/10.1890/04-1413
Modaresi Rad, A., Abatzoglou, J.T., Kreitler, J. et al. Human and infrastructure exposure to large wildfires in the United States. Nat Sustain 6, 1343–1351 (2023). https://doi.org/10.1038/s41893-023-01163-z
Radeloff, V. C., Helmers, D. P., Kramer, H. A., Mockrin, M. H., Alexandre, P. M., Bar-Massada, A., Butsic, V., Hawbaker, T. J., Martinuzzi, S., Syphard, A. D., & Stewart, S. I. (2018). Rapid growth of the US wildland-urban interface raises wildfire risk. Proceedings of the National Academy of Sciences of the United States of America, 115(13), 3314–3319. https://doi.org/10.1073/pnas.1718850115
Biswas, S., Hossain, M., & Zink, D. (2023). California wildfires, property damage, and mortgage repayment (Working Paper No. 2023-05). Federal Reserve Bank of Philadelphia. https://doi.org/10.21799/frbp.wp.2023.05
Jones, M. W., Smith, A. J. P., Betts, R., Canadell, J. G., Prentice, I. C., & Le Quéré, C. (2020). Climate change increases the risk of wildfires: January 2020. ScienceBrief. University of East Anglia. https://ueaeprints.uea.ac.uk/id/eprint/77982/
Sayad, Y., Mousannif, H., & Moatassime, H. (2019). Predictive modeling of wildfires: A new dataset and machine learning approach. Fire Safety Journal, 104, 130-146. https://doi.org/10.1016/j.firesaf.2019.01.006
Shadrin, D., Illarionova, S., Gubanov, F. et al. Wildfire spreading prediction using multimodal data and deep neural network approach. Sci Rep 14, 2606 (2024). https://doi.org/10.1038/s41598-024-52821-x
Published
How to Cite
Issue
Section
Copyright (c) 2025 Ayan Dalmia
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.
