has been cited by the following article(s):
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[1]
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Characterizing and Predicting Wildfire Evacuation Behavior: A Dual-Stage ML Approach
SoutheastCon 2026,
2026
DOI:10.1109/SoutheastCon63549.2026.11476300
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[2]
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Who leaves, when, and how? Dissecting evacuation choices from a survey of at-risk residents in the Western U.S.
Cities,
2026
DOI:10.1016/j.cities.2026.107038
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[3]
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From risk perception to readiness: understanding US wildfire preparedness drivers
Fire Ecology,
2026
DOI:10.1186/s42408-026-00467-7
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[4]
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Predicting Wildfire Spread with Machine Learning and a Socio-Ecological Approach to Identify Areas of High Vulnerability
2025 IEEE Conference on Technologies for Sustainability (SusTech),
2025
DOI:10.1109/SusTech63138.2025.11025662
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[5]
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Analyzing public response to wildfires: A socio-spatial study using SIR models and NLP techniques
Computers, Environment and Urban Systems,
2025
DOI:10.1016/j.compenvurbsys.2025.102333
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[6]
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Optimal allocation model of forest fire detection towers in protected areas based on fire occurrence risk: Where and how to act?
Canadian Journal of Forest Research,
2024
DOI:10.1139/cjfr-2023-0084
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[7]
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Evidence of increasing wildfire damage with decreasing property price in Southern California fires
PLOS ONE,
2024
DOI:10.1371/journal.pone.0300346
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[8]
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The intersection of socioeconomic status and wildfire risk: Insights from California
International Journal of ADVANCED AND APPLIED SCIENCES,
2024
DOI:10.21833/ijaas.2024.10.009
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