Register      Login
International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire

Articles citing this paper

Machine learning to predict final fire size at the time of ignition

Shane R. Coffield https://orcid.org/0000-0002-0550-5126 A D , Casey A. Graff https://orcid.org/0000-0002-2284-7363 B , Yang Chen https://orcid.org/0000-0002-0993-7081 A , Padhraic Smyth B , Efi Foufoula-Georgiou https://orcid.org/0000-0003-1078-231X C A and James T. Randerson https://orcid.org/0000-0001-6559-7387 A
+ Author Affiliations
- Author Affiliations

A Department of Earth System Science, Croul Hall, University of California, Irvine, CA 92697, USA.

B Department of Computer Science, Donald Bren Hall, University of California, Irvine, CA 92697, USA.

C Department of Civil and Environmental Engineering, Engineering Hall 5400, University of California, Irvine, CA 92697, USA.

D Corresponding author. Email: scoffiel@uci.edu

International Journal of Wildland Fire 28(11) 861-873 https://doi.org/10.1071/WF19023
Submitted: 16 February 2019  Accepted: 15 August 2019   Published: 17 September 2019



28 articles found in Crossref database.

Building a machine learning surrogate model for wildfire activities within a global Earth system model
Zhu Qing, Li Fa, Riley William J., Xu Li, Zhao Lei, Yuan Kunxiaojia, Wu Huayi, Gong Jianya, Randerson James
Geoscientific Model Development. 2022 15(5). p.1899
AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics
Li Fa, Zhu Qing, Riley William J., Zhao Lei, Xu Li, Yuan Kunxiaojia, Chen Min, Wu Huayi, Gui Zhipeng, Gong Jianya, Randerson James T.
Geoscientific Model Development. 2023 16(3). p.869
Forecasting Global Fire Emissions on Subseasonal to Seasonal (S2S) Time Scales
Chen Yang, Randerson James T., Coffield Shane R., Foufoula‐Georgiou Efi, Smyth Padhraic, Graff Casey A., Morton Douglas C., Andela Niels, van der Werf Guido R., Giglio Louis, Ott Lesley E.
Journal of Advances in Modeling Earth Systems. 2020 12(9).
Prediction of Bushfire Area Using Machine Learning Techniques
2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA) (2023)
Swain Debabrata, Kumar Manish, Jain Nikhil, Devnani Chirag
Prediction and data mining of burned areas of forest fires: Optimized data matching and mining algorithm provides valuable insight
Wood David A.
Artificial Intelligence in Agriculture. 2021 5 p.24
A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support
Bot Karol, Borges José G.
Inventions. 2022 7(1). p.15
Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models
Wang Sally S.‐C., Leung L. Ruby, Qian Yun
Journal of Geophysical Research: Atmospheres. 2023 128(14).
Developing Risk Assessment Framework for Wildfire in the United States – A Deep Learning Approach to Safety and Sustainability
Hu Pingfan, Tanchak Rachel, Wang Qingsheng
Journal of Safety and Sustainability. 2023
SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States
Buch Jatan, Williams A. Park, Juang Caroline S., Hansen Winslow D., Gentine Pierre
Geoscientific Model Development. 2023 16(12). p.3407
Minimal effect of prescribed burning on fire spread rate and intensity in savanna ecosystems
Moustakas Aristides, Davlias Orestis
Stochastic Environmental Research and Risk Assessment. 2021 35(4). p.849
Applying Machine Learning for Firebrand Production Prediction
Jha Anurag, Zhou Aixi
Fire Technology. 2022 58(5). p.3261
Building wildland–urban interface zone resilience through performance-based wildfire engineering. A holistic theoretical framework
Tampekis Stergios, Sakellariou Stavros, Palaiologou Palaiologos, Arabatzis Garyfallos, Kantartzis Apostolos, Malesios Chrisovalantis, Stergiadou Anastasia, Fafalis Dimitrios, Tsiaras Evangelos
Euro-Mediterranean Journal for Environmental Integration. 2023 8(3). p.675
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Tang Rongyun, Jin Mingzhou, Mao Jiafu, Ricciuto Daniel M., Chen Anping, Zhang Yulong
Geoscientific Model Development. 2024 17(4). p.1525
Predicting fire brigades' operations based on their type of interventions
2022 International Wireless Communications and Mobile Computing (IWCMC) (2022)
Mallouhy Roxane Elias, Guyeux Christophe, Jaoude Chady Abou, Makhoul Abdallah
An artificial intelligence framework for predicting fire spread sustainability in semiarid shrublands
Khanmohammadi Sadegh, Arashpour Mehrdad, Golafshani Emadaldin Mohammadi, Cruz Miguel G., Rajabifard Abbas
International Journal of Wildland Fire. 2023 32(4). p.636
A Novel Approach for Predicting Large Wildfires Using Machine Learning towards Environmental Justice via Environmental Remote Sensing and Atmospheric Reanalysis Data across the United States
Agrawal Nikita, Nelson Peder V., Low Russanne D.
Remote Sensing. 2023 15(23). p.5501
Forecasting Fire Using MobileNet Architecture
2023 7th International Conference on Electronics, Communication and Aerospace Technology (ICECA) (2023)
Sungeetha D., PushpaLatha S., Legapriyadharshini N., Akilandeswari A., Yamsani Nagendar, Padmakala S.
An Integrated Grassland Fire-Danger-Assessment System for a Mountainous National Park Using Geospatial Modelling Techniques
Mofokeng Olga D., Adelabu Samuel A., Jackson Colbert M.
Fire. 2024 7(2). p.61
Escalating carbon emissions from North American boreal forest wildfires and the climate mitigation potential of fire management
Phillips Carly A., Rogers Brendan M., Elder Molly, Cooperdock Sol, Moubarak Michael, Randerson James T., Frumhoff Peter C.
Science Advances. 2022 8(17).
Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables
Ji Yuheng, Wang Dan, Li Qingliang, Liu Taihui, Bai Yu
Forests. 2024 15(1). p.216
Multi-time Predictions of Wildfire Grid Map using Remote Sensing Local Data
2022 IEEE International Conference on Knowledge Graph (ICKG) (2022)
Yoon Hyung-Jin, Voulgaris Petros
A review of machine learning applications in wildfire science and management
Jain Piyush, Coogan Sean C.P., Subramanian Sriram Ganapathi, Crowley Mark, Taylor Steve, Flannigan Mike D.
Environmental Reviews. 2020 28(4). p.478
An enhanced method for predicting and analysing forest fires using an attention-based CNN model
Bhatt Shaifali, Chouhan Usha
Journal of Forestry Research. 2024 35(1).
Evaluating the Alaska Blocking Index as an indicator of wildfire potential in Alaska's central eastern interior
Ballinger Thomas J., Lader Rick T., Bieniek Peter A., Strader Heidi, Ziel Robert, Bhatt Uma S., Borries‐Strigle Cecilia, Hostler Joshua, Stevens Eric, Waigl Christine F., York Alison
International Journal of Climatology. 2024
Meteorological Environments Associated With California Wildfires and Their Potential Roles in Wildfire Changes During 1984–2017
Dong Lu, Leung L. Ruby, Qian Yun, Zou Yufei, Song Fengfei, Chen Xiaodong
Journal of Geophysical Research: Atmospheres. 2021 126(5).
Wildland Fire Burned Areas Prediction Using Long Short-Term Memory Neural Network with Attention Mechanism
Li Zhongzhi, Huang Yufeng, Li Xiaoxue, Xu Lei
Fire Technology. 2021 57(6). p.1
Assessing the predictive efficacy of six machine learning algorithms for the susceptibility of Indian forests to fire
Sharma Laxmi Kant, Gupta Rajit, Fatima Naureen
International Journal of Wildland Fire. 2022 31(8). p.735
Review of wildfire modeling considering effects on land surfaces
Or Dani, Furtak-Cole Eden, Berli Markus, Shillito Rose, Ebrahimian Hamed, Vahdat-Aboueshagh Hamid, McKenna Sean A.
Earth-Science Reviews. 2023 245 p.104569

Committee on Publication Ethics

Abstract Full Text PDF (953 KB) Export Citation Get Permission

Share

Share on Facebook Share on Twitter Share on LinkedIn Share via Email