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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:

Comparative analysis of gradient boosting algorithms for landslide susceptibility mapping
Emrehan Kutluğ Şahin
Geocarto International (2020) Vol. 37, Iss. 9, pp. 2441-2465
Closed Access | Times Cited: 116

Showing 1-25 of 116 citing articles:

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
A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey
R. Can, Sultan Kocaman, Candan Gökçeoğlu
Applied Sciences (2021) Vol. 11, Iss. 11, pp. 4993-4993
Open Access | Times Cited: 121
A comprehensive review of machine learning‐based methods in landslide susceptibility mapping
Songlin Liu, Luqi Wang, Wengang Zhang, et al.
Geological Journal (2023) Vol. 58, Iss. 6, pp. 2283-2301
Closed Access | Times Cited: 107
An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost
Selçuk Demir, Emrehan Kutluğ Şahin
Neural Computing and Applications (2022) Vol. 35, Iss. 4, pp. 3173-3190
Closed Access | Times Cited: 78
Comparison of histogram-based gradient boosting classification machine, random Forest, and deep convolutional neural network for pavement raveling severity classification
Nhat‐Duc Hoang, Tran Van-Duc
Automation in Construction (2023) Vol. 148, pp. 104767-104767
Closed Access | Times Cited: 65
Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms
Swapan Talukdar, Shahfahad, Shakeel Ahmed, et al.
Journal of Cleaner Production (2023) Vol. 406, pp. 136885-136885
Closed Access | Times Cited: 57
Effects of non-landslide sampling strategies on machine learning models in landslide susceptibility mapping
Tengfei Gu, Ping Duan, Mingguo Wang, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 18
Optimization of graphene-based biosensor design for haemoglobin detection using the gradient boosting algorithm for behaviour prediction
Jacob Wekalao, Guru Prasad Srinivasan, Shobhit K. Patel, et al.
Measurement (2024) Vol. 239, pp. 115452-115452
Closed Access | Times Cited: 17
Combining a class-weighted algorithm and machine learning models in landslide susceptibility mapping: A case study of Wanzhou section of the Three Gorges Reservoir, China
Huijuan Zhang, Yingxu Song, Shiluo Xu, et al.
Computers & Geosciences (2021) Vol. 158, pp. 104966-104966
Open Access | Times Cited: 70
Landslide Susceptibility Mapping Using Machine Learning Algorithm Validated by Persistent Scatterer In-SAR Technique
Muhammad Afaq Hussain, Zhanlong Chen, Ying Zheng, et al.
Sensors (2022) Vol. 22, Iss. 9, pp. 3119-3119
Open Access | Times Cited: 60
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
Assessing the imperative of conditioning factor grading in machine learning-based landslide susceptibility modeling: A critical inquiry
Taorui Zeng, Bijing Jin, Thomas Glade, et al.
CATENA (2023) Vol. 236, pp. 107732-107732
Closed Access | Times Cited: 38
Performance comparison of landslide susceptibility mapping under multiple machine-learning based models considering InSAR deformation: a case study of the upper Jinsha River
Jiaming Yao, Xin Yao, Zheng Zhao, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 24
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
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
Hybridizing genetic random forest and self-attention based CNN-LSTM algorithms for landslide susceptibility mapping in Darjiling and Kurseong, India
Armin Moghimi, Chiranjit Singha, Mahdiyeh Fathi, et al.
Quaternary Science Advances (2024) Vol. 14, pp. 100187-100187
Open 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
Comparison of diverse machine learning algorithms for forest fire susceptibility mapping in Antalya, Türkiye
Hazan Alkan Akıncı, Halil Akıncı, Mustafa Zeybek
Advances in Space Research (2024) Vol. 74, Iss. 2, pp. 647-667
Closed Access | Times Cited: 8
Prediction method for the dynamic response of expressway lateritic soil subgrades on the basis of Bayesian optimization CatBoost
Xuanjia Huang, Weizheng Liu, Qing Guo, et al.
Soil Dynamics and Earthquake Engineering (2024) Vol. 186, pp. 108943-108943
Closed Access | Times Cited: 8
Optimization of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility
R. S. Ajin, Samuele Segoni, Riccardo Fanti
Scientific Reports (2024) Vol. 14, Iss. 1
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
Landslide susceptibility assessment of the Wanzhou district: Merging landslide susceptibility modelling (LSM) with InSAR-derived ground deformation map
Chao Zhou, Lulu Gan, Ying Cao, et al.
International Journal of Applied Earth Observation and Geoinformation (2025) Vol. 136, pp. 104365-104365
Closed Access | Times Cited: 1
PS-InSAR-Based Validated Landslide Susceptibility Mapping along Karakorum Highway, Pakistan
Muhammad Afaq Hussain, Zhanlong Chen, Run Wang, et al.
Remote Sensing (2021) Vol. 13, Iss. 20, pp. 4129-4129
Open Access | Times Cited: 51
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
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