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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Quantifying the effects of environmental factors on wildfire burned area in the south central US using integrated machine learning techniques
Sing‐Chun Wang, Yuxuan Wang
Atmospheric chemistry and physics (2020) Vol. 20, Iss. 18, pp. 11065-11087
Open Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

Identifying Key Drivers of Wildfires in the Contiguous US Using Machine Learning and Game Theory Interpretation
Sing‐Chun Wang, Yun Qian, L. Ruby Leung, et al.
Earth s Future (2021) Vol. 9, Iss. 6
Open Access | Times Cited: 69
Estimation of potential wildfire behavior characteristics to assess wildfire danger in southwest China using deep learning schemes
Rui Chen, Binbin He, Yanxi Li, et al.
Journal of Environmental Management (2024) Vol. 351, pp. 120005-120005
Closed Access | Times Cited: 9
Winter and spring climate explains a large portion of interannual variability and trend in western U.S. summer fire burned area
Ronnie Abolafia‐Rosenzweig, Cenlin He, Fei Chen
Environmental Research Letters (2022) Vol. 17, Iss. 5, pp. 054030-054030
Open Access | Times Cited: 35
High-resolution mapping of wildfire drivers in California based on machine learning
Linghua Qiu, Ji Chen, Linfeng Fan, et al.
The Science of The Total Environment (2022) Vol. 833, pp. 155155-155155
Closed Access | Times Cited: 24
Interpreting machine learning prediction of fire emissions and comparison with FireMIP process-based models
Sing‐Chun Wang, Yun Qian, L. Ruby Leung, et al.
Atmospheric chemistry and physics (2022) Vol. 22, Iss. 5, pp. 3445-3468
Open Access | Times Cited: 21
Projection of Future Fire Emissions Over the Contiguous US Using Explainable Artificial Intelligence and CMIP6 Models
Sing‐Chun Wang, L. Ruby Leung, Yun Qian
Journal of Geophysical Research Atmospheres (2023) Vol. 128, Iss. 14
Open Access | Times Cited: 13
Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables over Northwestern Sichuan, China
Rui Chen, Binbin He, Xingwen Quan, et al.
International Journal of Disaster Risk Science (2023) Vol. 14, Iss. 2, pp. 313-325
Open Access | Times Cited: 10
Wildfire impacts on Spanish municipal population
Guillermo Peña
Journal of Environmental Management (2025) Vol. 377, pp. 124504-124504
Closed Access
Future fire-smoke PM2.5 health burden under climate change in Paraguay
Nicolás Borchers Arriagada, Paulina Schulz-Antipa, Mariana Conte-Grand
The Science of The Total Environment (2024) Vol. 924, pp. 171356-171356
Closed Access | Times Cited: 3
Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms
Xinming Lin, Jiwen Fan, Yuwei Zhang, et al.
Advances in Atmospheric Sciences (2024) Vol. 41, Iss. 7, pp. 1450-1462
Closed Access | Times Cited: 3
Integrating machine learning for enhanced wildfire severity prediction: A study in the Upper Colorado River basin
Heechan Han, Tadesse Alemayehu Abitew, Hadi Bazrkar, et al.
The Science of The Total Environment (2024) Vol. 952, pp. 175914-175914
Closed Access | Times Cited: 3
Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method
Fa Li, Qing Zhu, Kunxiaojia Yuan, et al.
Earth s Future (2024) Vol. 12, Iss. 10
Open Access | Times Cited: 3
Study on the temporal pattern and county-scale comprehensive risk assessment of wildfires in Sichuan Province
Weiting Yue, Yunji Gao, Yao Xiao, et al.
Research Square (Research Square) (2025)
Closed Access
Wildfire prediction using zero-inflated negative binomial mixed models: Application to Spain
María Bugallo, M. Dolores Esteban, Manuel Francisco Marey Pérez, et al.
Journal of Environmental Management (2022) Vol. 328, pp. 116788-116788
Open Access | Times Cited: 14
SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States
Jatan Buch, Park Williams, Caroline S. Juang, et al.
Geoscientific model development (2023) Vol. 16, Iss. 12, pp. 3407-3433
Open Access | Times Cited: 8
Projection of future fire emissions over the contiguous US using explainable artificial intelligence and CMIP6 models
Sing‐Chun Wang, L. Ruby Leung, Yun Qian
Authorea (Authorea) (2023)
Open Access | Times Cited: 5
Land, jet stream, and other atmospheric effects on burned area estimation during the South Asian heatwave of 2022
Amir Mustofa Irawan, M. Vall‐llossera, Carlos López-Martínez, et al.
International Journal of Applied Earth Observation and Geoinformation (2024) Vol. 128, pp. 103720-103720
Open Access | Times Cited: 1
Quantifying the Impacts of Fire‐Related Perturbations in WRF‐Hydro Terrestrial Water Budget Simulations in California's Feather River Basin
Ronnie Abolafia‐Rosenzweig, David Gochis, Andrew Schwarz, et al.
Hydrological Processes (2024) Vol. 38, Iss. 11
Closed Access | Times Cited: 1
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Rongyun Tang, Mingzhou Jin, Jiafu Mao, et al.
Geoscientific model development (2024) Vol. 17, Iss. 4, pp. 1525-1542
Open Access
Analysis of Trends in the Distance of Wildfires from Built-Up Areas in Spain and California (USA): 2007–2015
Manuel Francisco Marey Pérez, Óscar López-Álvarez, Luis Franco-Vázquez
Forests (2024) Vol. 15, Iss. 5, pp. 788-788
Open Access
Projecting large fires in the western US with a more trustworthy machine learning method
Fa Li, Qing Zhu, Kunxiaojia Yuan, et al.
Authorea (Authorea) (2024)
Open Access
Integrating Machine Learning for Enhanced Wildfire Severity Prediction: A Study in the Upper Colorado River Basin
Heechan Han, Tadesse Alemayehu Abitew, Hadi Bazrkar, et al.
(2024)
Closed Access
Modeling wildfire activity in the western United States with machine learning
Jatan Buch, Park Williams, Caroline S. Juang, et al.
(2022)
Open Access
Responses to Reviewers on "Interpreting machine learning prediction of fire emission and comparison with FireMIP process-based models"
Sing‐Chun Wang
(2021)
Open Access
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