Wildfire Data Analysis: Predicting Risk and Severity in San Diego County
Script
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| Scope And Content | Data analysis and engineering scripts. |
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Software and module version numbers used: |
Input data
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| Scope And Content | Historical fire perimeters and gridMET weather data. |
Output data
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| Scope And Content | Fire occurrence and severity predictions. |
Intermediate datasets
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| Scope And Content | Intermediate datasets computed from the input, and used in analysis or to generate predictions. |
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Gallaspy, Michael; Kannappan, Kevin; La Pierre, Martin; Maeouf, Sofean; Masilamani, Sathish; Altintas, Ilkay; Nguyen, Mai; Crawl, Dan; Corringham, Tom (2020). Wildfire Data Analysis: Predicting Risk and Severity in San Diego County. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0G15ZB3
- Description
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Major wildfires have grown in intensity and frequency not only in southern California, but also across the world, in the last few years. Property loss, damages and costs associated with these wildfires can be especially significant to residents, communities, public space, and the environment. Gas and electric companies have begun to proactively take down power grids as a preventative measure, a significant disruptor to everyday life. In this work, we analyze factors that affect wildfires and how changes in these factors over time affect wildfire behaviors and risks. Specifically, we took daily weather observations and associated fire measurements (indication of fire and the acres burned) in San Diego county over the course of the past 20 years, hypothesizing that changing weather conditions are the primary drivers of wildfires. We validate this theory by providing evidence that wildfire risk and severity are correlated with evapotranspiration (a measure of ground dryness), available vegetation, ambient temperature and wind speed. Using machine learning, we develop a robust model that detects the incidence and acres burned of a wildfire with a testing validation Recall score of 77%. Additionally, we pose a method to estimate the economic value of a region with associated “high” wildfire risks. Residents, firefighters, and public policy officials may leverage the results of this project for effective management and mitigation of wildfire risks.
- Creation Date
- 2019 to 2020
- Date Issued
- 2020
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- Language
- English
- Identifier
- Related Resources
- Abatzoglou, J. T. (2013), Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol., 33: 121131. https://www.climatologylab.org/gridmet.html
- California Department of Forestry and Fire Protection (CAL FIRE). CAL FIRE incident database. https://www.fire.ca.gov/
- Federal Emergency Management Agency (FEMA) National Structure Inventory (NSI): https://maps.crrel.usace.army.mil/apex/nsi_dev.cm2.map
- Geospatial Multi-Agency Coordination. GeoMAC incident database@https://www.geomac.gov/>
- References: Karevan, Zahra & Suykens, Johan. (2018). Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting. <
- U.S. Army Corps of Engineers "Planningt Community Toolkit:" https://planning.erdc.dren.mil/toolbox/tools.cfm?Id=179&Option=Planning%20Data%20Sets%20and%20Mapping%20Resources
- Karevan, Zahra & Suykens, Johan. (2018). Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting. https://arxiv.org/abs/1811.06341
- Khatter, S. (2016). Analyzing and Predicting Wildfires. UC San Diego. ProQuest ID: Khatter_ucsd_0033M_16738. Merritt ID: ark:/13030/m56h9c62. https://escholarship.org/uc/item/8xp9v2kr
- Li, Andy & Wang, Yuan & Yung, Yuk. (2019). Inducing Factors and Impacts of the October 2017 California Wildfires. Earth and Space Science. 6. http://doi.org/10.1029/2019EA000661
- Tourigny, E., Bedia, J., Bellprat, O., Caron, L. P., Doblas-Reyes, F. J. 2018. An observational study of the extreme wildfire events of California in 2017 : quantifying the relative importance of climate and weather. 20th EGU General Assembly, EGU2018, Proceedings from the conference held 4-13 April, 2018 in Vienna, Austria, p.9545. https://ui.adsabs.harvard.edu/abs/2018EGUGA..20.9545T
- GitHub repository with associated data analysis and feature engineering code: https://github.com/mcgallaspy/mas_dse_capstone/
Source data
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- License
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Creative Commons Attribution 4.0 International Public License
- Rights Holder
- Gallaspy, Michael; Kannappan, Kevin; La Pierre, Martin; Maeouf, Sofean; Masilamani, Sathish
- Copyright
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Under copyright (US)
Use: This work is available from the UC San Diego Library. This digital copy of the work is intended to support research, teaching, and private study.
Constraint(s) on Use: This work is protected by the U.S. Copyright Law (Title 17, U.S.C.). Use of this work beyond that allowed by "fair use" or any license applied to this work requires written permission of the copyright holder(s). Responsibility for obtaining permissions and any use and distribution of this work rests exclusively with the user and not the UC San Diego Library. Inquiries can be made to the UC San Diego Library program having custody of the work.
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Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp)
- Last Modified
2022-11-28