Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables
Omid Ghorbanzadeh, Thomas Blaschke, Khalil Gholamnia, et al.
Fire (2019) Vol. 2, Iss. 3, pp. 50-50
Open Access | Times Cited: 146

Showing 1-25 of 146 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: 547
Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region
Fatih Sivrikaya, Ömer Küçük
Ecological Informatics (2021) Vol. 68, pp. 101537-101537
Closed Access | Times Cited: 126
Forest Fire Risk Prediction: A Spatial Deep Neural Network-Based Framework
Mohsen Naderpour, Hossein Mojaddadi Rizeei, Fahimeh Ramezani
Remote Sensing (2021) Vol. 13, Iss. 13, pp. 2513-2513
Open Access | Times Cited: 105
A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions
Sepideh Tavakkoli Piralilou, Golzar Einali, Omid Ghorbanzadeh, et al.
Remote Sensing (2022) Vol. 14, Iss. 3, pp. 672-672
Open Access | Times Cited: 82
Advancements in Forest Fire Prevention: A Comprehensive Survey
Francesco Carta, Chiara Zidda, Martina Putzu, et al.
Sensors (2023) Vol. 23, Iss. 14, pp. 6635-6635
Open Access | Times Cited: 60
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
Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms
Mohd Rihan, Ahmed Ali Bindajam, Swapan Talukdar, et al.
Advances in Space Research (2023) Vol. 72, Iss. 2, pp. 426-443
Closed Access | Times Cited: 51
A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management
Sayed Pedram Haeri Boroujeni, Abolfazl Razi, Sahand Khoshdel, et al.
Information Fusion (2024) Vol. 108, pp. 102369-102369
Open Access | Times Cited: 47
Assessment of forest fire severity and land surface temperature using Google Earth Engine: a case study of Gujarat State, India
Keval H. Jodhani, H. V. Patel, Utsav Soni, et al.
Fire Ecology (2024) Vol. 20, Iss. 1
Open Access | Times Cited: 29
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
Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data
Bahareh Kalantar, Naonori Ueda, Mohammed Oludare Idrees, et al.
Remote Sensing (2020) Vol. 12, Iss. 22, pp. 3682-3682
Open Access | Times Cited: 139
Flood susceptibility mapping using an improved analytic network process with statistical models
Peyman Yariyan, Mohammadtaghi Avand, Rahim Ali Abbaspour, et al.
Geomatics Natural Hazards and Risk (2020) Vol. 11, Iss. 1, pp. 2282-2314
Open Access | Times Cited: 126
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
Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping
Khalil Gholamnia, Thimmaiah Gudiyangada Nachappa, Omid Ghorbanzadeh, et al.
Symmetry (2020) Vol. 12, Iss. 4, pp. 604-604
Open Access | Times Cited: 119
DEM resolution effects on machine learning performance for flood probability mapping
Mohammadtaghi Avand, Alban Kuriqi, Majid Khazaei, et al.
Journal of Hydro-environment Research (2021) Vol. 40, pp. 1-16
Open Access | Times Cited: 104
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
Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria
Thimmaiah Gudiyangada Nachappa, Omid Ghorbanzadeh, Khalil Gholamnia, et al.
Remote Sensing (2020) Vol. 12, Iss. 17, pp. 2757-2757
Open Access | Times Cited: 80
Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine
Andrea Sulova, Jamal Jokar Arsanjani
Remote Sensing (2020) Vol. 13, Iss. 1, pp. 10-10
Open Access | Times Cited: 72
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
Mapping China’s Forest Fire Risks with Machine Learning
Yakui Shao, Zhongke Feng, Linhao Sun, et al.
Forests (2022) Vol. 13, Iss. 6, pp. 856-856
Open Access | Times Cited: 52
A wildfire vulnerability index for buildings
Maria Papathoma-Köhle, Matthias Schlögl, Celine Garlichs, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 51
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
GIS-based forest fire risk determination for Milas district, Turkey
Mehmet Çetin, Özge Işık Pekkan, Mehtap Özenen Kavlak, et al.
Natural Hazards (2022) Vol. 119, Iss. 3, pp. 2299-2320
Closed Access | Times Cited: 42
Reimagining multi-criterion decision making by data-driven methods based on machine learning: A literature review
Huchang Liao, Yangpeipei He, Xueyao Wu, et al.
Information Fusion (2023) Vol. 100, pp. 101970-101970
Closed Access | Times Cited: 40
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
Page 1 - Next Page

Scroll to top