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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 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

Showing 1-25 of 365 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
Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms
Seyed Vahid Razavi Termeh, Aiding Kornejady, Hamid Reza Pourghasemi, et al.
The Science of The Total Environment (2017) Vol. 615, pp. 438-451
Closed Access | Times Cited: 429
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
Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability
Abolfazl Jaafari, Eric K. Zenner, Mahdi Panahi, et al.
Agricultural and Forest Meteorology (2019) Vol. 266-267, pp. 198-207
Closed Access | Times Cited: 265
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
Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)
Sasan Vafaei, Javad Soosani, Kamran Adeli, et al.
Remote Sensing (2018) Vol. 10, Iss. 2, pp. 172-172
Open Access | Times Cited: 237
Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility
Abolfazl Jaafari, Mahdi Panahi, Binh Thai Pham, et al.
CATENA (2019) Vol. 175, pp. 430-445
Closed Access | Times Cited: 236
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: 232
Integrated machine learning methods with resampling algorithms for flood susceptibility prediction
Esmaeel Dodangeh, Bahram Choubin, Ahmad Najafi Eigdir, et al.
The Science of The Total Environment (2019) Vol. 705, pp. 135983-135983
Closed Access | Times Cited: 223
Prediction of shear strength of soft soil using machine learning methods
Binh Thai Pham, Lê Hoàng Sơn, Tuan-Anh Hoang, et al.
CATENA (2018) Vol. 166, pp. 181-191
Closed Access | Times Cited: 204
Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park
Ljubomir Gigović, Hamid Reza Pourghasemi, Siniša Drobnjak, et al.
Forests (2019) Vol. 10, Iss. 5, pp. 408-408
Open Access | Times Cited: 199
Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods
Omid Rahmati, Bahram Choubin, Abolhasan Fathabadi, et al.
The Science of The Total Environment (2019) Vol. 688, pp. 855-866
Open Access | Times Cited: 198
Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
Hamid Reza Pourghasemi, Narges Kariminejad, Mahdis Amiri, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 198
Current status and applications of Artificial Intelligence (AI) in medical field: An overview
Abid Haleem, Mohd Javaid, Ibrahim Haleem Khan
Current Medicine Research and Practice (2019) Vol. 9, Iss. 6, pp. 231-237
Closed Access | Times Cited: 185
Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China
Haoyuan Hong, Paraskevas Tsangaratos, Ioanna Ilia, et al.
The Science of The Total Environment (2018) Vol. 630, pp. 1044-1056
Closed Access | Times Cited: 179
Artificial intelligence-based solutions for climate change: a review
Lin Chen, Zhonghao Chen, Yubing Zhang, et al.
Environmental Chemistry Letters (2023) Vol. 21, Iss. 5, pp. 2525-2557
Open Access | Times Cited: 168
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
Sustainable supplier selection under must-be criteria through Fuzzy inference system
Naveen Jain, A.R. Singh
Journal of Cleaner Production (2019) Vol. 248, pp. 119275-119275
Closed Access | Times Cited: 150
GIS-based forest fire risk mapping using the analytical network process and fuzzy logic
Hassan Abedi Gheshlaghi, Bakhtiar Feizizadeh, Thomas Blaschke
Journal of Environmental Planning and Management (2019) Vol. 63, Iss. 3, pp. 481-499
Closed Access | Times Cited: 147
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
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 Occurrence Prediction in China Based on Machine Learning Methods
Yongqi Pang, Yudong Li, Zhongke Feng, et al.
Remote Sensing (2022) Vol. 14, Iss. 21, pp. 5546-5546
Open Access | Times Cited: 81
Spatio-temporal assessment of land use land cover based on trajectories and cellular automata Markov modelling and its impact on land surface temperature of Lahore district Pakistan
Aqil Tariq, Faisal Mumtaz, Muhammad Majeed, et al.
Environmental Monitoring and Assessment (2022) Vol. 195, Iss. 1
Closed Access | Times Cited: 81
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: 52
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