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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:

GIS-based mineral prospectivity mapping using machine learning methods: A case study from Tongling ore district, eastern China
Tao Sun, Fei Chen, Lianxiang Zhong, et al.
Ore Geology Reviews (2019) Vol. 109, pp. 26-49
Open Access | Times Cited: 190

Showing 1-25 of 190 citing articles:

Metallogenic prediction based on fractal theory and machine learning in Duobaoshan Area, Heilongjiang Province
Jun Chen, Zhonghai Zhao, Yuanjiang Yang, et al.
Ore Geology Reviews (2024) Vol. 168, pp. 106030-106030
Open Access | Times Cited: 32
Mitigating uncertainties in mineral exploration targeting: Majority voting and confidence index approaches in the context of an exploration information system (EIS)
Mahyar Yousefi, Mark Lindsay, Oliver P. Kreuzer
Ore Geology Reviews (2024) Vol. 165, pp. 105930-105930
Open Access | Times Cited: 25
Simulation-based mineral prospectivity modeling and Gray Wolf optimization algorithm for delimiting exploration targets
Kamran Mostafaei, Mahyar Yousefi, Oliver P. Kreuzer, et al.
Ore Geology Reviews (2025), pp. 106458-106458
Open Access | Times Cited: 3
Data-Driven Predictive Modelling of Mineral Prospectivity Using Machine Learning and Deep Learning Methods: A Case Study from Southern Jiangxi Province, China
Tao Sun, Hui Li, Kaixing Wu, et al.
Minerals (2020) Vol. 10, Iss. 2, pp. 102-102
Open Access | Times Cited: 129
Data analysis methods for prospectivity modelling as applied to mineral exploration targeting: State-of-the-art and outlook
Mahyar Yousefi, Emmanuel John M. Carranza, Oliver P. Kreuzer, et al.
Journal of Geochemical Exploration (2021) Vol. 229, pp. 106839-106839
Closed Access | Times Cited: 101
A new strategy for spatial predictive mapping of mineral prospectivity: Automated hyperparameter tuning of random forest approach
Mehrdad Daviran, Abbas Maghsoudi, Reza Ghezelbash, et al.
Computers & Geosciences (2021) Vol. 148, pp. 104688-104688
Open Access | Times Cited: 94
Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation
Dahee Jung, Yosoon Choi
Minerals (2021) Vol. 11, Iss. 2, pp. 148-148
Open Access | Times Cited: 87
Ensemble learning models with a Bayesian optimization algorithm for mineral prospectivity mapping
Jiangning Yin, Nan Li
Ore Geology Reviews (2022) Vol. 145, pp. 104916-104916
Open Access | Times Cited: 66
A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data
Hojat Shirmard, Ehsan Farahbakhsh, Elnaz Heidari, et al.
Remote Sensing (2022) Vol. 14, Iss. 4, pp. 819-819
Open Access | Times Cited: 65
Regional-Scale Mineral Prospectivity Mapping: Support Vector Machines and an Improved Data-Driven Multi-criteria Decision-Making Technique
Reza Ghezelbash, Abbas Maghsoudi, Amirreza Bigdeli, et al.
Natural Resources Research (2021) Vol. 30, Iss. 3, pp. 1977-2005
Closed Access | Times Cited: 62
A Convolutional Neural Network of GoogLeNet Applied in Mineral Prospectivity Prediction Based on Multi-source Geoinformation
Na Yang, Zhenkai Zhang, Jianhua Yang, et al.
Natural Resources Research (2021) Vol. 30, Iss. 6, pp. 3905-3923
Closed Access | Times Cited: 62
Applications of data augmentation in mineral prospectivity prediction based on convolutional neural networks
Na Yang, Zhenkai Zhang, Jianhua Yang, et al.
Computers & Geosciences (2022) Vol. 161, pp. 105075-105075
Closed Access | Times Cited: 50
Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method
Maliheh Abbaszadeh, Saeed Soltani-Mohammadi, Ali Najah Ahmed
Computers & Geosciences (2022) Vol. 165, pp. 105140-105140
Closed Access | Times Cited: 39
A review on advancements in lithological mapping utilizing machine learning algorithms and remote sensing data
Mohamed Ali El-Omairi, Abdelkader El Garouani
Heliyon (2023) Vol. 9, Iss. 9, pp. e20168-e20168
Open Access | Times Cited: 34
Mineral prospectivity mapping using attention-based convolutional neural network
Quanke Li, Guoxiong Chen, Lei Luo
Ore Geology Reviews (2023) Vol. 156, pp. 105381-105381
Open Access | Times Cited: 29
Prospectivity and Uncertainty Analysis of Tungsten Polymetallogenic Mineral Resources in the Nanling Metallogenic Belt, South China: A Comparative Study of AdaBoost, GBDT, and XgBoost Algorithms
Tongfei Li, Qinglin Xia, Yongpeng Ouyang, et al.
Natural Resources Research (2024) Vol. 33, Iss. 3, pp. 1049-1071
Closed Access | Times Cited: 13
Fractal-Based Pattern Quantification of Mineral Grains: A Case Study of Yichun Rare-Metal Granite
Yue Liu, Tao Sun, Kaixing Wu, et al.
Fractal and Fractional (2024) Vol. 8, Iss. 1, pp. 49-49
Open Access | Times Cited: 9
Envisioning Human–Machine Relationship Towards Mining of the Future: An Overview
Peter Kolapo, Nafiu Olanrewaju Ogunsola, Kayode Komolafe, et al.
Mining (2025) Vol. 5, Iss. 1, pp. 5-5
Open Access | Times Cited: 1
A Novel Framework for Optimizing the Prediction of Areas Favorable to Porphyry-Cu Mineralization: Combination of Ant Colony and Grid Search Optimization Algorithms with Support Vector Machines
Sarina Akbari, Hamidreza Ramazi, Reza Ghezelbash
Natural Resources Research (2025)
Closed Access | Times Cited: 1
Convolutional neural network and transfer learning based mineral prospectivity modeling for geochemical exploration of Au mineralization within the Guandian–Zhangbaling area, Anhui Province, China
He Li, Xiaohui Li, Yuan Feng, et al.
Applied Geochemistry (2020) Vol. 122, pp. 104747-104747
Closed Access | Times Cited: 68
Mineral prospectivity mapping by deep learning method in Yawan-Daqiao area, Gansu
Yongyang Xu, Zixuan Li, Zhong Xie, et al.
Ore Geology Reviews (2021) Vol. 138, pp. 104316-104316
Closed Access | Times Cited: 50
Numerical characterization of groundwater flow and fracture-induced water inrush in tunnels
Zhen Huang, Kui Zhao, Xiaozhao Li, et al.
Tunnelling and Underground Space Technology (2021) Vol. 116, pp. 104119-104119
Closed Access | Times Cited: 50
Supervised mineral exploration targeting and the challenges with the selection of deposit and non-deposit sites thereof
Hossain Rahimi, Maysam Abedi, Mahyar Yousefi, et al.
Applied Geochemistry (2021) Vol. 128, pp. 104940-104940
Closed Access | Times Cited: 44
Polymetallic mineralization prospectivity modelling using multi-geospatial data in logistic regression: The Diapiric Zone, Northeastern Algeria
Mohammed Hichem Bencharef, Ahmed M. Eldosouky, Sara Zamzam, et al.
Geocarto International (2022) Vol. 37, Iss. 27, pp. 15392-15427
Closed Access | Times Cited: 32
Developments in Quantitative Assessment and Modeling of Mineral Resource Potential: An Overview
Yue Liu, Emmanuel John M. Carranza, Qinglin Xia
Natural Resources Research (2022) Vol. 31, Iss. 4, pp. 1825-1840
Open Access | Times Cited: 30
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