{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T16:22:35Z","timestamp":1784564555250,"version":"3.55.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Destructive wildfires result in billions of dollars in damage each year and are expected to increase in frequency, duration, and severity due to climate change. The current state-of-the-art wildfire spread models rely on mathematical growth predictions and physics-based models, which are difficult and computationally expensive to run. We present and evaluate a novel system, FireCast. FireCast combines artificial intelligence (AI) techniques with data collection strategies from geographic information systems (GIS). FireCast predicts which areas surrounding a burning wildfire have high-risk of near-future wildfire spread, based on historical fire data and using modest computational resources. FireCast is compared to a random prediction model and a commonly used wildfire spread model, Farsite, outperforming both with respect to total accuracy, recall, and F-score.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/636","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:46:05Z","timestamp":1564285565000},"page":"4575-4581","source":"Crossref","is-referenced-by-count":83,"title":["FireCast: Leveraging Deep Learning to Predict Wildfire Spread"],"prefix":"10.24963","author":[{"given":"David","family":"Radke","sequence":"first","affiliation":[{"name":"David R. Cheriton School of Computer Science, University of Waterloo"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Hessler","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Colorado College"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Ellsworth","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Colorado College"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T03:50:37Z","timestamp":1564285837000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/636"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/636","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}