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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:

Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)
Taşkın Kavzoǧlu, Alihan Teke
Arabian Journal for Science and Engineering (2022) Vol. 47, Iss. 6, pp. 7367-7385
Closed Access | Times Cited: 182

Showing 26-50 of 182 citing articles:

A hybrid residue based sequential encoding mechanism with XGBoost improved ensemble model for identifying 5-hydroxymethylcytosine modifications
Islam Uddin, Hamid Hussain Awan, Majdi Khalid, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 12
Explainability in wind farm planning: A machine learning framework for automatic site selection of wind farms
Atakan Bilgili, Tümay Arda, Batuhan Kılıç
Energy Conversion and Management (2024) Vol. 309, pp. 118441-118441
Closed Access | Times Cited: 11
Improving soil moisture prediction with deep learning and machine learning models
Fitsum T. Teshome, Haimanote K. Bayabil, Bruce Schaffer, et al.
Computers and Electronics in Agriculture (2024) Vol. 226, pp. 109414-109414
Closed Access | Times Cited: 10
Comparison of tree-based ensemble learning algorithms for landslide susceptibility mapping in Murgul (Artvin), Turkey
Ziya Usta, Halil Akıncı, Alper Tunga Akın
Earth Science Informatics (2024) Vol. 17, Iss. 2, pp. 1459-1481
Open Access | Times Cited: 9
Study on Landslide Susceptibility Based on Multi-Model Coupling: A Case Study of Sichuan Province, China
Jinming Zhang, Jianxi Qian, Yuefeng Lu, et al.
Sustainability (2024) Vol. 16, Iss. 16, pp. 6803-6803
Open Access | Times Cited: 8
Swarm optimization based heterogeneous machine learning techniques for enhanced landslide susceptibility assessment with comprehensive uncertainty quantification
Sumon Dey, Swarup Das
Earth Science Informatics (2025) Vol. 18, Iss. 1
Closed Access | Times Cited: 1
Landslide Susceptibility Mapping Using an LSTM Model with Feature-Selecting for the Yangtze River Basin in China
Peng Zuo, Wen Zhao, Wenjun Yan, et al.
Water (2025) Vol. 17, Iss. 2, pp. 167-167
Open Access | Times Cited: 1
Boosting-Based Machine Learning Applications in Polymer Science: A Review
Ivan Malashin, В С Тынченко, Andrei Gantimurov, et al.
Polymers (2025) Vol. 17, Iss. 4, pp. 499-499
Open Access | Times Cited: 1
Application of Tree-Based Ensemble Models to Landslide Susceptibility Mapping: A Comparative Study
Aihua Wei, Kaining Yu, Fenggang Dai, et al.
Sustainability (2022) Vol. 14, Iss. 10, pp. 6330-6330
Open Access | Times Cited: 34
Examining the Spatially Varying Relationships between Landslide Susceptibility and Conditioning Factors Using a Geographical Random Forest Approach: A Case Study in Liangshan, China
Xiaoliang Dai, Yunqiang Zhu, Kai Sun, et al.
Remote Sensing (2023) Vol. 15, Iss. 6, pp. 1513-1513
Open Access | Times Cited: 20
Potential impacts of future climate on the spatio-temporal variability of landslide susceptibility in Iran using machine learning algorithms and CMIP6 climate-change scenarios
Saeid Janizadeh, Sayed M. Bateni, Changhyun Jun, et al.
Gondwana Research (2023) Vol. 124, pp. 1-17
Closed Access | Times Cited: 18
A comparative study of regional landslide susceptibility mapping with multiple machine learning models
Yunhao Wang, Luqi Wang, Songlin Liu, et al.
Geological Journal (2023) Vol. 59, Iss. 9, pp. 2383-2400
Closed Access | Times Cited: 18
Comparison of Multiple Machine Learning Methods for Correcting Groundwater Levels Predicted by Physics-Based Models
Guanyin Shuai, Yan Zhou, Jingli Shao, et al.
Sustainability (2024) Vol. 16, Iss. 2, pp. 653-653
Open Access | Times Cited: 6
Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study
Rüstem Yılmaz, Fatma Hilal Yağın, Cemil Çolak, et al.
Frontiers in Medicine (2024) Vol. 11
Open Access | Times Cited: 6
Exploring the decision-making process of ensemble learning algorithms in landslide susceptibility mapping: Insights from local and global explainable AI analyses
Alihan Teke, Taşkın Kavzoǧlu
Advances in Space Research (2024) Vol. 74, Iss. 8, pp. 3765-3785
Closed Access | Times Cited: 6
Lifecycle research of social media rumor refutation effectiveness based on machine learning and visualization technology
Zongmin Li, Xinyu Du, Ye Zhao, et al.
Information Processing & Management (2022) Vol. 59, Iss. 6, pp. 103077-103077
Closed Access | Times Cited: 23
Evaluating causative factors for landslide susceptibility along the Imphal-Jiribam railway corridor in the North-Eastern part of India using a GIS-based statistical approach
Ankit Singh, Adaphro Ashuli, K. Niraj, et al.
Environmental Science and Pollution Research (2023) Vol. 31, Iss. 41, pp. 53767-53784
Closed Access | Times Cited: 15
Land subsidence susceptibility mapping in urban settlements using time-series PS-InSAR and random forest model
Fancheng Zhao, Fasheng Miao, Yiping Wu, et al.
Gondwana Research (2023) Vol. 125, pp. 406-424
Closed Access | Times Cited: 15
A comparative study of the XGBoost ensemble learning and multilayer perceptron in mineral prospectivity modeling: a case study of the Torud-Chahshirin belt, NE Iran
Amirreza Bigdeli, Abbas Maghsoudi, Reza Ghezelbash
Earth Science Informatics (2023) Vol. 17, Iss. 1, pp. 483-499
Closed Access | Times Cited: 15
GIS-Based Landslide Susceptibility Modeling: A Comparison between Best-First Decision Tree and Its Two Ensembles (BagBFT and RFBFT)
Jingyun Gui, Leandro R. Alejano, Miao Yao, et al.
Remote Sensing (2023) Vol. 15, Iss. 4, pp. 1007-1007
Open Access | Times Cited: 14
Application of artificial intelligence in geotechnical and geohazard investigations
Wengang Zhang, Biswajeet Pradhan, Bruno Stuyts, et al.
Geological Journal (2023) Vol. 58, Iss. 6, pp. 2187-2194
Closed Access | Times Cited: 14
Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods
Paul Esteban Bravo-López, Tomás Fernández, Chester Sellers, et al.
Land (2023) Vol. 12, Iss. 6, pp. 1135-1135
Open Access | Times Cited: 14
A new approach for landslide susceptibility assessments based on KDE-MDBN: A case study from mountainous regions impacted by the Wenchuan earthquake, China
Suyue Han, Bin Liu, Xinyue Fan, et al.
Environmental Modelling & Software (2023) Vol. 167, pp. 105759-105759
Closed Access | Times Cited: 14
Enhancing landslide management with hyper-tuned machine learning and deep learning models: Predicting susceptibility and analyzing sensitivity and uncertainty
Mohammed Dahim, Saeed Alqadhi, Javed Mallick
Frontiers in Ecology and Evolution (2023) Vol. 11
Open Access | Times Cited: 13
Brittleness index prediction using modified random forest based on particle swarm optimization of Upper Ordovician Wufeng to Lower Silurian Longmaxi shale gas reservoir in the Weiyuan Shale Gas Field, Sichuan Basin, China
Mbula Ngoy Nadege, Shu Jiang, Grant Charles Mwakipunda, et al.
Geoenergy Science and Engineering (2023) Vol. 233, pp. 212518-212518
Closed Access | Times Cited: 13

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