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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 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

Showing 20 citing articles:

Integrating geospatial, remote sensing, and machine learning for climate-induced forest fire susceptibility mapping in Similipal Tiger Reserve, India
Chiranjit Singha, Kishore Chandra Swain, Armin Moghimi, et al.
Forest Ecology and Management (2024) Vol. 555, pp. 121729-121729
Open Access | Times Cited: 27
Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis
Hoang Thi Hang, Javed Mallick, Saeed Alqadhi, et al.
Environmental Technology & Innovation (2024) Vol. 35, pp. 103655-103655
Open Access | Times Cited: 20
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
SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye
Muzaffer Can İban, Oktay Aksu
Remote Sensing (2024) Vol. 16, Iss. 15, pp. 2842-2842
Open Access | Times Cited: 10
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 | Times Cited: 1
Brittleness index prediction using modified random forest based on particle swarm optimization of Upper Ordovician Wufeng to Lower Silurian Longmaxi shale gas reservoir in the Weiyuan Shale Gas Field, Sichuan Basin, China
Mbula Ngoy Nadege, Shu Jiang, Grant Charles Mwakipunda, et al.
Geoenergy Science and Engineering (2023) Vol. 233, pp. 212518-212518
Closed Access | Times Cited: 17
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
Vegetation Classification and a Biomass Inversion Model for Wildfires in Chongli Based on Remote Sensing Data
Feng Xu, Wenjing Chen, Rui Xie, et al.
Fire (2024) Vol. 7, Iss. 2, pp. 58-58
Open Access | Times Cited: 4
Advancing the LightGBM approach with three novel nature-inspired optimizers for predicting wildfire susceptibility in Kauaʻi and Molokaʻi Islands, Hawaii
Saeid Janizadeh, Trang Thi Kieu Tran, Sayed M. Bateni, et al.
Expert Systems with Applications (2024) Vol. 258, pp. 124963-124963
Closed Access | Times Cited: 4
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
A spatial weight sampling method integrating the spatiotemporal pattern enhances the understanding of the occurrence mechanism of wildfires in the southwestern mountains of China
Wenlong Yang, Mingshan Wu, Lei Kong, et al.
Forest Ecology and Management (2025) Vol. 585, pp. 122619-122619
Closed Access
Forest Fire Prediction Based on Time Series Networks and Remote Sensing Images
Yue Cao, Xuanyu Zhou, Yanqi Yu, et al.
Forests (2024) Vol. 15, Iss. 7, pp. 1221-1221
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
Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation
Yunjie Mu, Liyuan Ou, Wenjing Chen, et al.
Drones (2024) Vol. 8, Iss. 4, pp. 142-142
Open Access | Times Cited: 2
A temporal perspective on the reliability of wildfire hazard assessment based on machine learning and remote sensing data
Zakaria Matougui, Mohamed Zouidi
Earth Science Informatics (2024) Vol. 18, Iss. 1
Closed Access | Times Cited: 2
Landslide Susceptibility Mapping and Interpretation in the Upper Minjiang River Basin
Xin Wang, Shibiao Bai
Remote Sensing (2023) Vol. 15, Iss. 20, pp. 4947-4947
Open Access | Times Cited: 3
Advances in remote sensing for sustainable forest management: monitoring and protecting natural resources
Rogério Pinto Espíndola, Nelson F. F. Ebecken
Revista Caribeña de Ciencias Sociales (2023) Vol. 12, Iss. 4, pp. 1605-1617
Open Access | Times Cited: 2
Forest Fire Susceptibility Zonation using dNBR and Machine Learning models: A case study at the Similipal Biosphere Reserve, Odisha, India
Rajkumar Guria, Manoranjan Mishra, Samiksha Mohanta, et al.
(2024)
Closed Access
Synergistic approaches in forest fire risk mapping using fuzzy AHP and machine learning models in the Chure Tarai Madhesh Landscape (CTML) of Nepal
Milan Dhakal, Balram Bhatta, Prakash Lamichhane, et al.
Geomatics Natural Hazards and Risk (2024) Vol. 15, Iss. 1
Open Access
An interpretable machine learning model for predicting forest fire danger based on bayesian optimization
Zhiyang Liu, Kuibin Zhou, Qichao Yao, et al.
Emergency Management Science and Technology (2020), pp. 1-12
Open Access | Times Cited: 1
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