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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 1-25 of 182 citing articles:

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: 108
Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey
Moziihrii Ado, Khwairakpam Amitab, Arnab Kumar Maji, et al.
Remote Sensing (2022) Vol. 14, Iss. 13, pp. 3029-3029
Open Access | Times Cited: 106
Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations
Halit Enes Aydin, Muzaffer Can İban
Natural Hazards (2022) Vol. 116, Iss. 3, pp. 2957-2991
Closed Access | Times Cited: 83
Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost)
Taşkın Kavzoǧlu, Alihan Teke
Bulletin of Engineering Geology and the Environment (2022) Vol. 81, Iss. 5
Closed Access | Times Cited: 81
Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms
Xu Guo, Xiaofan Gui, Hanxiang Xiong, et al.
Journal of Hydrology (2023) Vol. 621, pp. 129599-129599
Closed Access | Times Cited: 74
Prediction based mean-value-at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets
Jyotirmayee Behera, Ajit Kumar Pasayat, Harekrushna Behera, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 120, pp. 105843-105843
Closed Access | Times Cited: 69
A LightGBM-based landslide susceptibility model considering the uncertainty of non-landslide samples
Deliang Sun, WU Xiao-qing, Haijia Wen, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 49
Machine learning-based characterization of hydrochar from biomass: Implications for sustainable energy and material production
Alireza Shafizadeh, Hossein Shahbeik, Shahin Rafiee, et al.
Fuel (2023) Vol. 347, pp. 128467-128467
Open Access | Times Cited: 44
High resolution landslide susceptibility mapping using ensemble machine learning and geospatial big data
Nirdesh Sharma, Manabendra Saharia, G. V. Ramana
CATENA (2023) Vol. 235, pp. 107653-107653
Open Access | Times Cited: 41
Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface
Zhenfeng Shao, Muhammad Nasar Ahmad, Akib Javed
Remote Sensing (2024) Vol. 16, Iss. 4, pp. 665-665
Open Access | Times Cited: 38
A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department
Zahra Rahmatinejad, Toktam Dehghani, Benyamin Hoseini, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 23
A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering
Elaheh Yaghoubi, Elnaz Yaghoubi, Ahmed A. Khamees, et al.
Neural Computing and Applications (2024) Vol. 36, Iss. 21, pp. 12655-12699
Open Access | Times Cited: 19
Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan
Nafees Ali, Jian Chen, Xiaodong Fu, et al.
Remote Sensing (2024) Vol. 16, Iss. 6, pp. 988-988
Open Access | Times Cited: 16
XGBoost-enhanced ensemble model using discriminative hybrid features for the prediction of sumoylation sites
Salman Khan, Sumaiya Noor, Tahir Javed, et al.
BioData Mining (2025) Vol. 18, Iss. 1
Open Access | Times Cited: 2
Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations
Sheng Dong, Afaq Khattak, Irfan Ullah, et al.
International Journal of Environmental Research and Public Health (2022) Vol. 19, Iss. 5, pp. 2925-2925
Open Access | Times Cited: 68
Biomass microwave pyrolysis characterization by machine learning for sustainable rural biorefineries
Yadong Yang, Hossein Shahbeik, Alireza Shafizadeh, et al.
Renewable Energy (2022) Vol. 201, pp. 70-86
Closed Access | Times Cited: 52
Assessment of rainfall-induced landslide susceptibility in Artvin, Turkey using machine learning techniques
Halil Akıncı
Journal of African Earth Sciences (2022) Vol. 191, pp. 104535-104535
Closed Access | Times Cited: 40
SBAS-InSAR based validated landslide susceptibility mapping along the Karakoram Highway: a case study of Gilgit-Baltistan, Pakistan
Isma Kulsoom, Weihua Hua, Sadaqat Hussain, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 38
Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach
Muzaffer Can İban, Süleyman Sefa Bilgilioğlu
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 6, pp. 2243-2270
Closed Access | Times Cited: 34
Intelligent prediction model of mechanical properties of ultrathin niobium strips based on XGBoost ensemble learning algorithm
Zhenhua Wang, Yunfei Liu, Tao Wang, et al.
Computational Materials Science (2023) Vol. 231, pp. 112579-112579
Closed Access | Times Cited: 29
Does machine learning adequately predict earthquake induced landslides?
Ajaya Pyakurel, Bhim Kumar Dahal, Dipendra Gautam
Soil Dynamics and Earthquake Engineering (2023) Vol. 171, pp. 107994-107994
Closed Access | Times Cited: 23
Comparative Analysis of Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping: A Case Study in Rize, Turkey
Ayşe Yavuz Özalp, Halil Akıncı, Mustafa Zeybek
Water (2023) Vol. 15, Iss. 14, pp. 2661-2661
Open Access | Times Cited: 22
Rapid Landslide Extraction from High-Resolution Remote Sensing Images Using SHAP-OPT-XGBoost
Na Lin, Di Zhang, Shanshan Feng, et al.
Remote Sensing (2023) Vol. 15, Iss. 15, pp. 3901-3901
Open Access | Times Cited: 22
Application and interpretability of ensemble learning for landslide susceptibility mapping along the Three Gorges Reservoir area, China
Bo Liu, Haixiang Guo, Jinling Li, et al.
Natural Hazards (2024) Vol. 120, Iss. 5, pp. 4601-4632
Closed Access | Times Cited: 14
Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques
Piotr Myśliwiec, Andrzej Kubit, Paulina Szawara
Materials (2024) Vol. 17, Iss. 7, pp. 1452-1452
Open Access | Times Cited: 14
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