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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:

Role of Machine Learning Algorithms in Forest Fire Management: A Literature Review
Muhammad Arif, Alghamdi Khloud K, Sahel Salma A, et al.
Journal of Robotics and Automation (2021) Vol. 5, Iss. 1
Open Access | Times Cited: 47

Showing 1-25 of 47 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: 66
Fight Fire with Fire: Detecting Forest Fires with Embedded Machine Learning Models Dealing with Audio and Images on Low Power IoT Devices
Giacomo Peruzzi, Alessandro Pozzebon, Mattia Van Der Meer
Sensors (2023) Vol. 23, Iss. 2, pp. 783-783
Open Access | Times Cited: 44
Autonomous Unmanned Aerial Vehicles in Bushfire Management: Challenges and Opportunities
Shouthiri Partheepan, Farzad Sanati, Jahan Hassan
Drones (2023) Vol. 7, Iss. 1, pp. 47-47
Open Access | Times Cited: 42
Drones and Blockchain Integration to Manage Forest Fires in Remote Regions
Dena Mahmudnia, Mehrdad Arashpour, Yu Bai, et al.
Drones (2022) Vol. 6, Iss. 11, pp. 331-331
Open Access | Times Cited: 24
Intelligent Technologies, Enzyme-Embedded and Microbial Degradation of Agricultural Plastics
Chrysanthos Maraveas, Marianna Kotzabasaki, Thomas Bartzanas
AgriEngineering (2023) Vol. 5, Iss. 1, pp. 85-111
Open Access | Times Cited: 16
Using ensemble machine learning algorithm to predict forest fire occurrence probability in Madhya Pradesh and Chhattisgarh, India
Sumedha Surbhi Singh, C. Jeganathan
Advances in Space Research (2023) Vol. 73, Iss. 6, pp. 2969-2987
Closed Access | Times Cited: 16
Machine learning algorithms applied to wildfire data in California's central valley
Kassandra Hernández, Aaron B. Hoskins
Trees Forests and People (2024) Vol. 15, pp. 100516-100516
Open Access | Times Cited: 6
Identifying Influential Spatial Drivers of Forest Fires through Geographically and Temporally Weighted Regression Coupled with a Continuous Invasive Weed Optimization Algorithm
Parham Pahlavani, Amin Raei, Behnaz Bigdeli, et al.
Fire (2024) Vol. 7, Iss. 1, pp. 33-33
Open Access | Times Cited: 4
Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire Prediction
İrem Üstek, Miguel Arana‐Catania, Alexander Farr, et al.
Earth and Space Science (2024) Vol. 11, Iss. 11
Open Access | Times Cited: 4
A Highly Accurate Forest Fire Prediction Model Based on an Improved Dynamic Convolutional Neural Network
Shaoxiong Zheng, Peng Gao, Weixing Wang, et al.
Applied Sciences (2022) Vol. 12, Iss. 13, pp. 6721-6721
Open Access | Times Cited: 18
Fire Risk Mapping Using Machine Learning Method and Remote Sensing in the Mediterranean Region
Fatih Sivrikaya, Döndü Demirel
Advances in Space Research (2025)
Closed Access
A Multi-Criteria Forest Fire Danger Assessment System on GIS Using Literature-Based Model and Analytical Hierarchy Process Model for Mediterranean Coast of Manavgat, Türkiye
İzzet Ersoy, Emre Ünsal, Önder Gürsoy
Sustainability (2025) Vol. 17, Iss. 5, pp. 1971-1971
Open Access
Machine Learning-Based Wildfire Susceptibility Mapping: A Gis-Integrated Predictive Framework
Yehya Bouzeraa, Nardjes Bouchemal, Salim Djaaboub
(2025)
Closed Access
Fire Intensity and spRead forecAst (FIRA): A Machine Learning Based Fire Spread Prediction Model for Air Quality Forecasting Application
Wei‐Ting Hung, Barry Baker, Patrick Campbell, et al.
GeoHealth (2025) Vol. 9, Iss. 3
Open Access
Disaster identification scheme based on federated learning and Cognitive Internet of Vehicles
Muhammad Junaid Anjum, Muhammad Shoaib Farooq, T. Umer, et al.
Computer Communications (2025), pp. 108216-108216
Closed Access
Enhancing predictive ability of optimized group method of data handling (GMDH) method for wildfire susceptibility mapping
Trang Thi Kieu Tran, Sayed M. Bateni, Fatemeh Rezaie, et al.
Agricultural and Forest Meteorology (2023) Vol. 339, pp. 109587-109587
Closed Access | Times Cited: 9
Predicting the Occurrence of Forest Fire in the Central-South Region of China
Quansheng Hai, Xiufeng Han, Battsengel Vandansambuu, et al.
(2024)
Open Access | Times Cited: 3
Predicting the Occurrence of Forest Fire in the Central-South Region of China
Quansheng Hai, Xiufeng Han, Battsengel Vandansambuu, et al.
Forests (2024) Vol. 15, Iss. 5, pp. 844-844
Open Access | Times Cited: 3
Facing the Wildfire Spread Risk Challenge: Where Are We Now and Where Are We Going?
Jingjing Sun, Wenwen Qi, Yuandong Huang, et al.
Fire (2023) Vol. 6, Iss. 6, pp. 228-228
Open Access | Times Cited: 8
Applications of Artificial Intelligence in Fire Safety of Agricultural Structures
Chrysanthos Maraveas, Dimitrios Loukatos, Thomas Bartzanas, et al.
Applied Sciences (2021) Vol. 11, Iss. 16, pp. 7716-7716
Open Access | Times Cited: 17
Improving the Accuracy of Random Forest Classifier for Identifying Burned Areas in the Tangier-Tetouan-Al Hoceima Region Using Google Earth Engine
Houda Badda, El Khalil Cherif, Hakim Boulaassal, et al.
Remote Sensing (2023) Vol. 15, Iss. 17, pp. 4226-4226
Open Access | Times Cited: 7
An efficient, multi-scale neighbourhood index to quantify wildfire likelihood
Douglas Radford, Holger R. Maier, Hedwig van Delden, et al.
International Journal of Wildland Fire (2024) Vol. 33, Iss. 5
Open Access | Times Cited: 2
Contribution of biophysical and climate variables to the spatial distribution of wildfires in Iran
Kobra Shojaeizadeh, Mahmoud Ahmadi, Abbasali Dadashi-Roudbari
Journal of Forestry Research (2023) Vol. 34, Iss. 6, pp. 1763-1775
Closed Access | Times Cited: 5
A Fully Automatic, Interpretable and Adaptive Machine Learning Approach to Map Burned Area from Remote Sensing
Daniela Stroppiana, Gloria Bordogna, Matteo Sali, et al.
ISPRS International Journal of Geo-Information (2021) Vol. 10, Iss. 8, pp. 546-546
Open Access | Times Cited: 11
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