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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 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

Showing 1-25 of 173 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
Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping
Faming Huang, Zhongshan Cao, Jianfei Guo, et al.
CATENA (2020) Vol. 191, pp. 104580-104580
Closed Access | Times Cited: 389
A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping
Dieu Tien Bui, Phuong Thao Thi Ngo, Tien Dat Pham, et al.
CATENA (2019) Vol. 179, pp. 184-196
Closed Access | Times Cited: 270
Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area
Meriame Mohajane, Romulus Costache, Firoozeh Karimi, et al.
Ecological Indicators (2021) Vol. 129, pp. 107869-107869
Open Access | Times Cited: 262
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
Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches
Omid Ghorbanzadeh, Khalil Valizadeh Kamran, Thomas Blaschke, et al.
Fire (2019) Vol. 2, Iss. 3, pp. 43-43
Open Access | Times Cited: 165
Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility
Hossein Moayedi, Mohammad Mehrabi, Dieu Tien Bui, et al.
Journal of Environmental Management (2020) Vol. 260, pp. 109867-109867
Closed Access | Times Cited: 141
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
Leveraging the power of internet of things and artificial intelligence in forest fire prevention, detection, and restoration: A comprehensive survey
Sofia Giannakidou, Panagiotis Radoglou-Grammatikis, Θωμάς Λάγκας, et al.
Internet of Things (2024) Vol. 26, pp. 101171-101171
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
A Novel Swarm Intelligence—Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility
Dieu Tien Bui, Hossein Moayedi, Bahareh Kalantar, et al.
Sensors (2019) Vol. 19, Iss. 16, pp. 3590-3590
Open Access | Times Cited: 134
Spatial pattern analysis and prediction of forest fire using new machine learning approach of Multivariate Adaptive Regression Splines and Differential Flower Pollination optimization: A case study at Lao Cai province (Viet Nam)
Dieu Tien Bui, Nhat‐Duc Hoang, Pijush Samui
Journal of Environmental Management (2019) Vol. 237, pp. 476-487
Closed Access | Times Cited: 123
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
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
A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China
Wei Xie, Xiaoshuang Li, Wenbin Jian, et al.
ISPRS International Journal of Geo-Information (2021) Vol. 10, Iss. 2, pp. 93-93
Open Access | Times Cited: 91
Forest 4.0: Digitalization of forest using the Internet of Things (IoT)
Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram, et al.
Journal of King Saud University - Computer and Information Sciences (2021) Vol. 34, Iss. 8, pp. 5587-5601
Open Access | Times Cited: 91
Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India
A.L. Achu, Jobin Thomas, C. D. Aju, et al.
Ecological Informatics (2021) Vol. 64, pp. 101348-101348
Closed Access | Times Cited: 84
Exploring machine learning potential for climate change risk assessment
Federica Zennaro, Elisa Furlan, Christian Simeoni, et al.
Earth-Science Reviews (2021) Vol. 220, pp. 103752-103752
Closed Access | Times Cited: 82
Identification of torrential valleys using GIS and a novel hybrid integration of artificial intelligence, machine learning and bivariate statistics
Romulus Costache, Haoyuan Hong, Yi Wang
CATENA (2019) Vol. 183, pp. 104179-104179
Closed Access | Times Cited: 78
Using machine learning algorithms to map the groundwater recharge potential zones
Hamid Reza Pourghasemi, Nitheshnirmal Sãdhasivam, Saleh Yousefi, et al.
Journal of Environmental Management (2020) Vol. 265, pp. 110525-110525
Closed Access | Times Cited: 75
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
Damming effect on habitat quality of riparian corridor
Swades Pal, Swapan Talukdar, Ripan Ghosh
Ecological Indicators (2020) Vol. 114, pp. 106300-106300
Open Access | Times Cited: 71
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
Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models
Susanta Mahato, Swades Pal, Swapan Talukdar, et al.
Geoscience Frontiers (2021) Vol. 12, Iss. 5, pp. 101175-101175
Open Access | Times Cited: 68
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