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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:

Application of machine learning models in the behavioral study of forest fires in the Brazilian Federal District region
Jesús N.S. Rubí, Paulo H.P. de Carvalho, Paulo Roberto de Lira Gondim
Engineering Applications of Artificial Intelligence (2022) Vol. 118, pp. 105649-105649
Closed Access | Times Cited: 32

Showing 1-25 of 32 citing articles:

A Brief Review of Machine Learning Algorithms in Forest Fires Science
Ramez Alkhatib, Wahib Sahwan, Anas Alkhatieb, et al.
Applied Sciences (2023) Vol. 13, Iss. 14, pp. 8275-8275
Open Access | Times Cited: 64
Machine Learning for Early Fire Detection in the Oasis Environment
Safae Sossi Alaoui, Yousef Farhaoui
Lecture notes in networks and systems (2024), pp. 138-143
Closed Access | Times Cited: 21
Comparing machine learning algorithms to predict vegetation fire detections in Pakistan
Fahad Shahzad, Kaleem Mehmood, Khadim Hussain, et al.
Fire Ecology (2024) Vol. 20, Iss. 1
Open Access | Times Cited: 13
Coupling machine learning and theoretical models to compare key properties of biochar in adsorption kinetics rate and maximum adsorption capacity for emerging contaminants
Bingyou Liu, F. Xi, Huanjing Zhang, et al.
Bioresource Technology (2024) Vol. 402, pp. 130776-130776
Closed Access | Times Cited: 9
Machine Learning for Predicting Forest Fire Occurrence in Changsha: An Innovative Investigation into the Introduction of a Forest Fuel Factor
Xin Wu, Gui Zhang, Yang Zhi-gao, et al.
Remote Sensing (2023) Vol. 15, Iss. 17, pp. 4208-4208
Open Access | Times Cited: 19
A Forest Fire Prediction Model Based on Cellular Automata and Machine Learning
Xuan Sun, Ning Li, Duoqi Chen, et al.
IEEE Access (2024) Vol. 12, pp. 55389-55403
Open Access | Times Cited: 6
Combination four different ensemble algorithms with the generalized linear model (GLM) for predicting forest fire susceptibility
Saeid Janizadeh, Sayed M. Bateni, Changhyun Jun, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 13
Spatial risk assessment of maritime transportation in offshore waters of China using machine learning and geospatial big data
Xiao Zhou
Ocean & Coastal Management (2023) Vol. 247, pp. 106934-106934
Closed Access | Times Cited: 12
Forecast Zoning of Forest Fire Occurrence: A Case Study in Southern China
Xiaodong Jing, Xusheng Li, Donghui Zhang, et al.
Forests (2024) Vol. 15, Iss. 2, pp. 265-265
Open Access | Times Cited: 4
PREDICTION OF FOREST FIRE SUSCEPTIBILITY USING MACHINE LEARNING TOOLS IN THE TRIUNFO DO XINGU ENVIRONMENTAL PROTECTION AREA, AMAZON, BRAZIL
Kemuel Maciel Freitas, Ronie Silva Juvanhol, Christiano Jorge Gomes Pinheiro, et al.
Journal of South American Earth Sciences (2025), pp. 105366-105366
Closed Access
Modeling of Forest Fire Risk Areas of Amazonas Department, Peru: Comparative Evaluation of Three Machine Learning Methods
Alex J. Vergara, Sivmny V. Valqui-Reina, Dennis Cieza-Tarrillo, et al.
Forests (2025) Vol. 16, Iss. 2, pp. 273-273
Open Access
Wildfire impacts on Spanish municipal population
Guillermo Peña
Journal of Environmental Management (2025) Vol. 377, pp. 124504-124504
Closed Access
SafetyGPT: An autonomous agent of electrical safety risks for monitoring workers’ unsafe behaviors
Wei Li, Fuqi Ma, Zhou Zuo, et al.
International Journal of Electrical Power & Energy Systems (2025) Vol. 168, pp. 110672-110672
Closed Access
Spatiotemporal dynamics of coastal dead zones in the Gulf of Mexico over 20 years using remote sensing
Yingjie Li, Zilong Xia, Lan Huu Nguyen, et al.
The Science of The Total Environment (2025) Vol. 979, pp. 179461-179461
Closed Access
AutoST-Net: A Spatiotemporal Feature-Driven Approach for Accurate Forest Fire Spread Prediction from Remote Sensing Data
Xuexue Chen, Ye Tian, Change Zheng, et al.
Forests (2024) Vol. 15, Iss. 4, pp. 705-705
Open Access | Times Cited: 3
FC-StackGNB: A novel machine learning modeling framework for forest fire risk prediction combining feature crosses and model fusion algorithm
Ye Su, Longlong Zhao, Xiaoli Li, et al.
Ecological Indicators (2024), pp. 112577-112577
Open Access | Times Cited: 3
New forest fire assessment model based on artificial neural network and analytic hierarchy process or fuzzy-analytic hierarchy process methodology for fire vulnerability map
Meryem Tahri, Sanaa Badr, Zohreh Mohammadi, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 138, pp. 109399-109399
Closed Access | Times Cited: 3
Assessing Fire Risk in Wildland–Urban Interface Regions Using a Machine Learning Method and GIS data: The Example of Istanbul’s European Side
Ercüment Aksoy, Abdülkadir Koçer, İsmail Yilmaz, et al.
Fire (2023) Vol. 6, Iss. 10, pp. 408-408
Open Access | Times Cited: 8
Data-Driven Approaches for Wildfire Mapping and Prediction Assessment Using a Convolutional Neural Network (CNN)
Rida Kanwal, Warda Rafaqat, Mansoor Iqbal, et al.
Remote Sensing (2023) Vol. 15, Iss. 21, pp. 5099-5099
Open Access | Times Cited: 8
An Innovative Investigation on Predicting Forest Fire Using Machine Learning Approach
Renugadevi Ramalingam
Advances in computational intelligence and robotics book series (2024), pp. 61-77
Closed Access | Times Cited: 2
Research on predicting the diffusion of toxic heavy gas sulfur dioxide by applying a hybrid deep learning model to real case data
Yuchen Wang, Zhengshan Luo, Jihao Luo
The Science of The Total Environment (2023) Vol. 901, pp. 166506-166506
Closed Access | Times Cited: 6
Urban tree failure probability prediction based on dendrometric aspects and machine learning models
Danilo Samuel Jodas, Sérgio Brazolin, Giuliana Del Nero Velasco, et al.
Computers Environment and Urban Systems (2024) Vol. 108, pp. 102074-102074
Closed Access | Times Cited: 1
Prediction of Fire Occurrences Based on Multi-layer Perceptron Using Social Indicators
Chu Zhang, Won-Hwa Hong, Young‐Hoon Bae
Fire Science and Engineering (2024) Vol. 38, Iss. 1, pp. 51-58
Open Access | Times Cited: 1
Analyzing the dynamics of forest fires in Málaga province: assessing the interplay of vegetation and human influence on regional hazard trends over three decades
María Teresa González Moreno, José María Senciales González
Euro-Mediterranean Journal for Environmental Integration (2024)
Open Access | Times Cited: 1
Research on fire accident prediction and risk assessment algorithm based on data mining and machine learning
Ziyang Zhang, Lingye Tan, Robert L. K. Tiong
Advances in Continuous and Discrete Models (2024) Vol. 2024, Iss. 1
Open Access | Times Cited: 1
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