Predicting Wildfire Susceptibility in Napa County, California using Machine Learning
DOI:
https://doi.org/10.47611/jsrhs.v11i3.3868Keywords:
wildfire prediction, machine learning, random forest, Napa CountyAbstract
Wildfires have long been a part of the natural environment, however through climate change and increased human activity, they have become a significant problem to both humans and wildland. Stopping the expansion of wildfires would be critical in mitigating the dangerous outcomes of them. Firefighters stopping the spread of wildfires must know which parts of the environment are most vulnerable to the spread of wildfires, and vegetation is one of the key determining factors in the wildfire susceptibility of a given area. Previous works have used several different machine learning algorithms for the purpose of determining wildfire susceptibility. The algorithm used in this study for wildfire susceptibility prediction is a random forest applied to a vegetation dataset of Napa County, California provided by the California Department of Fish and Wildlife (CDFW). The random forest works by creating a set of decision trees to get an overall probability for each vegetation area. The model has a 91.7% accuracy in predicting wildfire burn probability in a vegetation area.
Downloads
References or Bibliography
Pausas, J. G., & Keeley, J. E. (2019). Wildfires as an ecosystem service. Frontiers in Ecology and the Environment, 17(5), 289-295. doi:10.1002/fee.2044
Muller, C. H., Hanawalt, R. B., & McPherson, J. K. (1968). Allelopathic control of herb growth in the fire cycle of California chaparral. Bulletin of the Torrey Botanical Club, 225-231. doi:10.2307/2483669
Miller, J. D., & Safford, H. (2012). Trends in wildfire severity: 1984 to 2010 in the Sierra Nevada, Modoc Plateau, and southern Cascades, California, USA. Fire ecology, 8(3), 41-57. doi:10.4996/fireecology.0803041
Kramer, H. A., Mockrin, M. H., Alexandre, P. M., & Radeloff, V. C. (2019). High wildfire damage in interface communities in California. International journal of wildland fire, 28(9), 641-650. doi:10.1071/WF18108
Amatulli, G., Rodrigues, M. J., Trombetti, M., & Lovreglio, R. (2006). Assessing long‐term fire risk at local scale by means of decision tree technique. Journal of Geophysical Research: Biogeosciences, 111(G4). doi:10.1029/2005JG000133
Jain, P., Coogan, S. C., Subramanian, S. G., Crowley, M., Taylor, S., & Flannigan, M. D. (2020). A review of machine learning applications in wildfire science and management. Environmental Reviews, 28(4), 478-505. doi:10.1139/er-2020-0019
Bustillo Sánchez, M., Tonini, M., Mapelli, A., & Fiorucci, P. (2021). Spatial assessment of wildfires susceptibility in Santa Cruz (Bolivia) using random forest. Geosciences, 11(5), 224. doi:10.3390/geosciences11050224
Ma, W., Feng, Z., Cheng, Z., Chen, S., & Wang, F. (2020). Identifying forest fire driving factors and related impacts in china using random forest algorithm. Forests, 11(5), 507. doi:10.3390/f11050507
Collins, L., Griffioen, P., Newell, G., & Mellor, A. (2018). The utility of Random Forests for wildfire severity mapping. Remote Sensing of Environment, 216, 374-384. doi:10.1016/j.rse.2018.07.005
Malik, A., Rao, M. R., Puppala, N., Koouri, P., Thota, V. A. K., Liu, Q., ... & Gao, J. (2021). Data-driven wildfire risk prediction in northern california. Atmosphere, 12(1), 109. doi:10.3390/atmos12010109
Thorne, J. (2020, Jul. 22). Vegetation - Napa County Update 2016 [ds2899]. CDFW Vegetation Classification and Mapping Program. Retrieved July 29, 2022 from http://bios.dfg.ca.gov
Barrette, J., August, P., & Golet, F. (2000). Accuracy assessment of wetland boundary delineation using aerial photography and digital orthophotography. Photogrammetric Engineering and Remote Sensing, 66(4), 409-416. doi:00099-1112/00/6504-409$3.00/0
Sawyer, J.O., Keeler-Wolf, T., & Evens, J.M. (2009). A Manual of California Vegetation, Second Edition. Sacramento: California Native Plant Society
Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS journal of photogrammetry and remote sensing, 114, 24-31. doi:10.1016/j.isprsjprs.2016.01.011
Husted, A. (2022, Sep. 20). U.S. Wildfire Prediction. https://github.com/jalexander03/dsc-5-capstone-project-online-ds-ft-041519
USDA Forest Service (2022, Jul. 14). Fire Occurrence FIRESTAT Yearly. USDA Forest Service. Retrieved July 29, 2022 from https://data.fs.usda.gov
Francos, M., Pereira, P., Mataix-Solera, J., Arcenegui, V., Alcañiz, M., & Úbeda, X. (2018). How clear-cutting affects fire severity and soil properties in a Mediterranean ecosystem. Journal of environmental management, 206, 625-632. doi:10.1016/j.jenvman.2017.11.011
Published
How to Cite
Issue
Section
Copyright (c) 2022 Stefan Shakeri; Krti Tallam
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.
