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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:

Machine Learning Methods and Synthetic Data Generation to Predict Large Wildfires
Fernando Pérez Porras, Paula Triviño-Tarradas, Carmen Cima-Rodríguez, et al.
Sensors (2021) Vol. 21, Iss. 11, pp. 3694-3694
Open Access | Times Cited: 49

Showing 1-25 of 49 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
Detection of forest fire using deep convolutional neural networks with transfer learning approach
Hatice Çatal Reis, Veysel Turk
Applied Soft Computing (2023) Vol. 143, pp. 110362-110362
Closed Access | Times Cited: 45
A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support
Karol Bot, José G. Borges
Inventions (2022) Vol. 7, Iss. 1, pp. 15-15
Open Access | Times Cited: 70
EAACI guidelines on environmental science in allergic diseases and asthma – Leveraging artificial intelligence and machine learning to develop a causality model in exposomics
Mohamed H. Shamji, Markus Ollert, Ian M. Adcock, et al.
Allergy (2023) Vol. 78, Iss. 7, pp. 1742-1757
Open Access | Times Cited: 32
Forest fire risk assessment model optimized by stochastic average gradient descent
Zexin Fu, Adu Gong, Jia Wan, et al.
Ecological Indicators (2025) Vol. 170, pp. 113006-113006
Open Access | Times Cited: 1
Comparison of tabular synthetic data generation techniques using propensity and cluster log metric
Aryan Pathare, Ramchandra Mangrulkar, Kartik Suvarna, et al.
International Journal of Information Management Data Insights (2023) Vol. 3, Iss. 2, pp. 100177-100177
Open Access | Times Cited: 21
Classification and detection of natural disasters using machine learning and deep learning techniques: A review
Kibitok Abraham, Moataz M. Abdelwahab, Mohammed Abo‐Zahhad
Earth Science Informatics (2023) Vol. 17, Iss. 2, pp. 869-891
Closed Access | Times Cited: 17
Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model
Xingdong Li, Mingxian Zhang, Shiyu Zhang, et al.
Fire (2022) Vol. 5, Iss. 1, pp. 13-13
Open Access | Times Cited: 27
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
Physics Informed Machine Learning (PIML) for Design, Management and Resilience-Development of Urban Infrastructures: Concept, State-of-the-Art, Challenges and Opportunities
Alvin Wei Ze Chew, Renfei He, Limao Zhang
Archives of Computational Methods in Engineering (2024)
Closed Access | Times Cited: 5
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
Wildfire impacts on Spanish municipal population
Guillermo Peña
Journal of Environmental Management (2025) Vol. 377, pp. 124504-124504
Closed Access
Enhancing Wildfire Prevention and Grassland Burning Management with Synthetic Data Generation Algorithms for Predictive Fire Danger Index Modeling
Mayowa B George, Matthew Onuh Ijiga, O. H. Adeyemi
(2025), pp. 1930-1944
Closed Access
Advancements in Artificial Intelligence Applications for Forest Fire Prediction
Hui Liu, Lifu Shu, Xiaodong Liu, et al.
Forests (2025) Vol. 16, Iss. 4, pp. 704-704
Open Access
A Generalized Spatiotemporally Weighted Boosted Regression to Predict the Occurrence of Grassland Fires in the Mongolian Plateau
Renguang Wu, Zhimin Hong, Wala Du, et al.
Remote Sensing (2025) Vol. 17, Iss. 9, pp. 1485-1485
Open Access
Polycyclic aromatic hydrocarbon occurrence in forest soils in response to fires: a summary across sites
Biwei Yang, Yameng Shi, Shan Xu, et al.
Environmental Science Processes & Impacts (2021) Vol. 24, Iss. 1, pp. 32-41
Closed Access | Times Cited: 21
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: 9
PVS-GEN: Systematic Approach for Universal Synthetic Data Generation Involving Parameterization, Verification, and Segmentation
Kyung Min Kim, Jong Wook Kwak
Sensors (2024) Vol. 24, Iss. 1, pp. 266-266
Open Access | Times Cited: 3
FSNet: Enhancing Forest-Fire and Smoke Detection with an Advanced UAV-Based Network
Donghua Wu, Zhongmin Qian, Dongyang Wu, et al.
Forests (2024) Vol. 15, Iss. 5, pp. 787-787
Open Access | Times Cited: 3
WSMOTER: a novel approach for imbalanced regression
Luís Camacho, Fernando Bação
Applied Intelligence (2024) Vol. 54, Iss. 19, pp. 8789-8799
Open 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
Drivers of Wildfire Spatial Expansion: Modeling Insights from Semi-Arid Oak Forests of W Iran
Akram Sadeghi, Mozhgan Ahmadi Nadoushan, Naser Ahmadi Sani
Advances in Space Research (2025)
Closed Access
Tackling the Wildfire Prediction Challenge: An Explainable Artificial Intelligence (XAI) Model Combining Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP) for Enhanced Interpretability and Accuracy
Bin Liao, Tao Zhou, Yanping Liu, et al.
Forests (2025) Vol. 16, Iss. 4, pp. 689-689
Open 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
An artificial intelligence framework for predicting fire spread sustainability in semiarid shrublands
Sadegh Khanmohammadi, Mehrdad Arashpour, Emadaldin Mohammadi Golafshani, et al.
International Journal of Wildland Fire (2023) Vol. 32, Iss. 4, pp. 636-649
Closed Access | Times Cited: 8
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