Under copyright 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. 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.
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. Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) 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
Type
dataset
Identifier
ark:/20775/bb3077072p
Language
English
Subject
Wildfires Weather Fire risk Fire severity Fire occurrence Data Science & Engineering Master of Advanced Study (DSE MAS) San Diego County DSE MAS - 2020 Cohort
If you're wondering about permissions and what you can do with this item, a good starting point is the "rights information" on this page. See our terms of use for more tips.
Share your story
Has Calisphere helped you advance your research, complete a project, or find something meaningful? We'd love to hear about it; please send us a message.
