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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:

A Survey of Machine Learning Algorithms Based Forest Fires Prediction and Detection Systems
Faroudja Abid
Fire Technology (2020) Vol. 57, Iss. 2, pp. 559-590
Closed Access | Times Cited: 156

Showing 1-25 of 156 citing articles:

Forest fire and smoke detection using deep learning-based learning without forgetting
Sathishkumar Veerappampalayam Easwaramoorthy, Jaehyuk Cho, Malliga Subramanian, et al.
Fire Ecology (2023) Vol. 19, Iss. 1
Open Access | Times Cited: 136
Reworking the political in digital forests: The cosmopolitics of socio-technical worlds
Jennifer Gabrys, Michelle Westerlaken, Danilo Urzedo, et al.
Progress in Environmental Geography (2022) Vol. 1, Iss. 1-4, pp. 58-83
Open Access | Times Cited: 77
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 review on enhancing energy efficiency and adaptability through system integration for smart buildings
Um-e-Habiba, Ijaz Ahmed, Mohammad Asif, et al.
Journal of Building Engineering (2024) Vol. 89, pp. 109354-109354
Closed Access | Times Cited: 37
Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI)
Hanyu Li, Stenka Vulova, Alby Duarte Rocha, et al.
The Science of The Total Environment (2024) Vol. 916, pp. 170330-170330
Open Access | Times Cited: 24
Real-time forecast of tunnel fire scenario and hazard based on external smoke images
Jiaqi Cheng, Yang Nie, Saihua Jiang, et al.
Tunnelling and Underground Space Technology (2025) Vol. 158, pp. 106377-106377
Closed Access | Times Cited: 2
Forest fire probability zonation using dNBR and machine learning models: a case study at the Similipal Biosphere Reserve (SBR), Odisha, India
Rajkumar Guria, Manoranjan Mishra, Samiksha Mohanta, et al.
Environmental Science and Pollution Research (2025)
Closed Access | Times Cited: 2
Advancing forest fire prediction: A multi-layer stacking ensemble model approach
Fahad Shahzad, Kaleem Mehmood, Shoaib Ahmad Anees, et al.
Earth Science Informatics (2025) Vol. 18, Iss. 3
Closed Access | Times Cited: 2
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: 69
Multi-Scale Forest Fire Recognition Model Based on Improved YOLOv5s
Chen Gong, Hang Zhou, Zhongyuan Li, et al.
Forests (2023) Vol. 14, Iss. 2, pp. 315-315
Open Access | Times Cited: 30
A hybrid method for fire detection based on spatial and temporal patterns
Pedro Vinícius Almeida Borges de Venâncio, Roger Júnio Campos, Tamires Martins Rezende, et al.
Neural Computing and Applications (2023) Vol. 35, Iss. 13, pp. 9349-9361
Closed Access | Times Cited: 27
Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China
Chaoxue Tan, Zhongke Feng
Sustainability (2023) Vol. 15, Iss. 7, pp. 6292-6292
Open Access | Times Cited: 27
Ensembling machine learning models to identify forest fire-susceptible zones in Northeast India
Mriganka Shekhar Sarkar, Bishal Kumar Majhi, Bhawna Pathak, et al.
Ecological Informatics (2024) Vol. 81, pp. 102598-102598
Open Access | Times Cited: 16
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: 15
Fire and smoke detection from videos: A literature review under a novel taxonomy
Diego Gragnaniello, Antonio Greco, Carlo Sansone, et al.
Expert Systems with Applications (2024) Vol. 255, pp. 124783-124783
Open Access | Times Cited: 9
Efficient forest fire detection based on an improved YOLO model
Lei Cao, Zirui Shen, Sheng Xu
Visual Intelligence (2024) Vol. 2, Iss. 1
Open Access | Times Cited: 9
Study on Small-Scale Forest Fire Risk Zoning Based on Random Forest and the Fuzzy Analytic Network Process
Dai Chen, Aicong Zeng, Yan He, et al.
Forests (2025) Vol. 16, Iss. 1, pp. 97-97
Open Access | Times Cited: 1
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
Predictive model of spatial scale of forest fire driving factors: a case study of Yunnan Province, China
Wenhui Li, Quanli Xu, Junhua Yi, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 31
Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review
Santos D. Chicas, Jonas Østergaard Nielsen
Natural Hazards (2022) Vol. 114, Iss. 3, pp. 2417-2434
Open Access | Times Cited: 29
MTL-FFDET: A Multi-Task Learning-Based Model for Forest Fire Detection
Kangjie Lu, Jingwen Huang, Junhui Li, et al.
Forests (2022) Vol. 13, Iss. 9, pp. 1448-1448
Open Access | Times Cited: 29
A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas
Tran Xuan Truong, Viet‐Ha Nhu, Doan Thi Nam Phuong, et al.
Remote Sensing (2023) Vol. 15, Iss. 14, pp. 3458-3458
Open Access | Times Cited: 20
A Forest Fire Susceptibility Modeling Approach Based on Integration Machine Learning Algorithm
Changjiang Shi, Fuquan Zhang
Forests (2023) Vol. 14, Iss. 7, pp. 1506-1506
Open Access | Times Cited: 20
A Forest Fire Recognition Method Based on Modified Deep CNN Model
Shaoxiong Zheng, Xiangjun Zou, Peng Gao, et al.
Forests (2024) Vol. 15, Iss. 1, pp. 111-111
Open Access | Times Cited: 7
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