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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:

Wildfire ignition in the forests of southeast China: Identifying drivers and spatial distribution to predict wildfire likelihood
Futao Guo, Zhangwen Su, Guangyu Wang, et al.
Applied Geography (2015) Vol. 66, pp. 12-21
Closed Access | Times Cited: 100

Showing 1-25 of 100 citing articles:

A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area
Dieu Tien Bui, Quang‐Thanh Bui, Quoc-Phi Nguyen, et al.
Agricultural and Forest Meteorology (2016) Vol. 233, pp. 32-44
Closed Access | Times Cited: 365
Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China
Guoli Zhang, Ming Wang, Kai Liu
International Journal of Disaster Risk Science (2019) Vol. 10, Iss. 3, pp. 386-403
Open Access | Times Cited: 268
A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data
Mahyat Shafapour Tehrany, Simon Jones, Farzin Shabani, et al.
Theoretical and Applied Climatology (2018) Vol. 137, Iss. 1-2, pp. 637-653
Closed Access | Times Cited: 173
Simulation of forest fire spread based on artificial intelligence
Zechuan Wu, Bin Wang, Mingze Li, et al.
Ecological Indicators (2022) Vol. 136, pp. 108653-108653
Open Access | Times Cited: 91
Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey
Hazan Alkan Akıncı, Halil Akıncı
Earth Science Informatics (2023) Vol. 16, Iss. 1, pp. 397-414
Closed Access | Times Cited: 53
Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression
Dieu Tien Bui, Kim-Thoa Le, Van Sinh Nguyen, et al.
Remote Sensing (2016) Vol. 8, Iss. 4, pp. 347-347
Open Access | Times Cited: 168
Human-caused fire occurrence modelling in perspective: a review
Sergi Costafreda-Aumedes, Carles Comas, Cristina Vega‐García
International Journal of Wildland Fire (2017) Vol. 26, Iss. 12, pp. 983-983
Open Access | Times Cited: 150
Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method
Slobodan Milanović, Nenad Marković, Dragan Pamučar, et al.
Forests (2020) Vol. 12, Iss. 1, pp. 5-5
Open Access | Times Cited: 122
Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling
Hamid Reza Pourghasemi, Amiya Gayen, Rosa Lasaponara, et al.
Environmental Research (2020) Vol. 184, pp. 109321-109321
Closed Access | Times Cited: 109
Understanding fire drivers and relative impacts in different Chinese forest ecosystems
Futao Guo, Zhangwen Su, Guangyu Wang, et al.
The Science of The Total Environment (2017) Vol. 605-606, pp. 411-425
Closed Access | Times Cited: 101
Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal
A. V. Parajuli, Ambika P. Gautam, Sundar Prasad Sharma, et al.
Geomatics Natural Hazards and Risk (2020) Vol. 11, Iss. 1, pp. 2569-2586
Open Access | Times Cited: 100
Explainable artificial intelligence (XAI) detects wildfire occurrence in the Mediterranean countries of Southern Europe
Roberto Cilli, Mario Elia, Marina D’Este, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 50
Forest Fire Spread Monitoring and Vegetation Dynamics Detection Based on Multi-Source Remote Sensing Images
Yuping Tian, Zechuan Wu, Mingze Li, et al.
Remote Sensing (2022) Vol. 14, Iss. 18, pp. 4431-4431
Open Access | Times Cited: 48
Comparison of diverse machine learning algorithms for forest fire susceptibility mapping in Antalya, Türkiye
Hazan Alkan Akıncı, Halil Akıncı, Mustafa Zeybek
Advances in Space Research (2024) Vol. 74, Iss. 2, pp. 647-667
Closed Access | Times Cited: 10
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
Spatial prediction of forest fires in India: a machine learning approach for improved risk assessment and early warning systems
Utsav Biswas, Susanta Mahato, P. K. Joshi
Environmental Science and Pollution Research (2025)
Closed Access | Times Cited: 1
Estimating the probability of wildfire occurrence in Mediterranean landscapes using Artificial Neural Networks
Mario Elia, Marina D’Este, Davide Ascoli, et al.
Environmental Impact Assessment Review (2020) Vol. 85, pp. 106474-106474
Closed Access | Times Cited: 62
A new approach of deep neural computing for spatial prediction of wildfire danger at tropical climate areas
Van Hung Le, Duc A. Hoang, Chuyen Trung Tran, et al.
Ecological Informatics (2021) Vol. 63, pp. 101300-101300
Closed Access | Times Cited: 55
Assessment of China’s forest fire occurrence with deep learning, geographic information and multisource data
Yakui Shao, Zhichao Wang, Zhongke Feng, et al.
Journal of Forestry Research (2022) Vol. 34, Iss. 4, pp. 963-976
Closed Access | Times Cited: 37
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
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
Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil's Federal District
Pablo Pozzobon de, Osmar Abílio de Carvalho Júnior, Eraldo Aparecido Trondoli Matricardi, et al.
International Journal of Wildland Fire (2018) Vol. 28, Iss. 1, pp. 35-35
Closed Access | Times Cited: 58
Drivers of forest fire occurrence in the cultural landscape of Central Europe
Martin Adámek, Zuzana Jankovská, Věroslava Hadincová, et al.
Landscape Ecology (2018) Vol. 33, Iss. 11, pp. 2031-2045
Closed Access | Times Cited: 57
Comparative analysis of spatial variation in forest fire drivers between boreal and subtropical ecosystems in China
Zhangwen Su, Mulualem Tigabu, Qianqian Cao, et al.
Forest Ecology and Management (2019) Vol. 454, pp. 117669-117669
Closed Access | Times Cited: 49
Spatial and temporal variation of air pollutant emissions from forest fires in China
Rong Song, Tijian Wang, Juncai Han, et al.
Atmospheric Environment (2022) Vol. 281, pp. 119156-119156
Closed Access | Times Cited: 28
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