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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:

Spatial pattern assessment of tropical forest fire danger at Thuan Chau area (Vietnam) using GIS-based advanced machine learning algorithms: A comparative study
Nguyễn Ngọc Thạch, Dang Bao-Toan Ngo, Pham Xuan-Canh, et al.
Ecological Informatics (2018) Vol. 46, pp. 74-85
Open Access | Times Cited: 153

Showing 1-25 of 153 citing articles:

A review of machine learning applications in wildfire science and management
Piyush Jain, Sean C. P. Coogan, Sriram Ganapathi Subramanian, et al.
Environmental Reviews (2020) Vol. 28, Iss. 4, pp. 478-505
Open Access | Times Cited: 550
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
Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction
Binh Thai Pham, Abolfazl Jaafari, Mohammadtaghi Avand, et al.
Symmetry (2020) Vol. 12, Iss. 6, pp. 1022-1022
Open Access | Times Cited: 231
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
Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey
Muzaffer Can İban, Aliihsan Şekertekin
Ecological Informatics (2022) Vol. 69, pp. 101647-101647
Closed Access | Times Cited: 112
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
Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization
Khabat Khosravi, Mahdi Panahi, Dieu Tien Bui
Hydrology and earth system sciences (2018) Vol. 22, Iss. 9, pp. 4771-4792
Open Access | Times Cited: 156
A novel hybrid approach of Bayesian Logistic Regression and its ensembles for landslide susceptibility assessment
Mousa Abedini, Bahareh Ghasemian, Ataollah Shirzadi, et al.
Geocarto International (2018) Vol. 34, Iss. 13, pp. 1427-1457
Closed Access | Times Cited: 131
A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy
Marj Tonini, Mirko D’Andrea, Guido Biondi, et al.
Geosciences (2020) Vol. 10, Iss. 3, pp. 105-105
Open Access | Times Cited: 123
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
Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree
Xuan Luan Truong, Muneki Mitamura, Yasuyuki Kono, et al.
Applied Sciences (2018) Vol. 8, Iss. 7, pp. 1046-1046
Open Access | Times Cited: 100
A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides
Dieu Tien Bui, Himan Shahabi, Ataollah Shirzadi, et al.
Remote Sensing (2018) Vol. 10, Iss. 10, pp. 1538-1538
Open Access | Times Cited: 99
GIS-based spatial prediction of tropical forest fire danger using a new hybrid machine learning method
Dieu Tien Bui, Van Hung Le, Nhat‐Duc Hoang
Ecological Informatics (2018) Vol. 48, pp. 104-116
Open Access | Times Cited: 96
Deep neural networks for global wildfire susceptibility modelling
Guoli Zhang, Ming Wang, Kai Liu
Ecological Indicators (2021) Vol. 127, pp. 107735-107735
Open Access | Times Cited: 94
A deep learning ensemble model for wildfire susceptibility mapping
Alexandra Bjånes, Rodrigo De la Fuente, Pablo Mena
Ecological Informatics (2021) Vol. 65, pp. 101397-101397
Closed Access | Times Cited: 91
Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm
Tran Thi Tuyen, Abolfazl Jaafari, Hoang Phan Hải Yen, et al.
Ecological Informatics (2021) Vol. 63, pp. 101292-101292
Closed Access | Times Cited: 90
A real-time forecast of tunnel fire based on numerical database and artificial intelligence
Xiqiang Wu, Xiaoning Zhang, Xinyan Huang, et al.
Building Simulation (2021) Vol. 15, Iss. 4, pp. 511-524
Closed Access | Times Cited: 81
Prediction of gully erosion susceptibility mapping using novel ensemble machine learning algorithms
Alireza Arabameri, Subodh Chandra Pal, Romulus Costache, et al.
Geomatics Natural Hazards and Risk (2021) Vol. 12, Iss. 1, pp. 469-498
Open Access | Times Cited: 77
Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods
Qian He, Ziyu Jiang, Ming Wang, et al.
Remote Sensing (2021) Vol. 13, Iss. 8, pp. 1572-1572
Open Access | Times Cited: 64
Parallel SVM model for forest fire prediction
Kajol Singh, K. P. Neethu, K. Madhurekaa, et al.
Soft Computing Letters (2021) Vol. 3, pp. 100014-100014
Open Access | Times Cited: 59
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
Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National Level
Andrea Trucchia, Giorgio Meschi, Paolo Fiorucci, et al.
Fire (2022) Vol. 5, Iss. 1, pp. 30-30
Open Access | Times Cited: 48
Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping
Yongze Song, Margaret Kalácska, Mateo Gašparović, et al.
International Journal of Applied Earth Observation and Geoinformation (2023) Vol. 120, pp. 103300-103300
Open Access | Times Cited: 41
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
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