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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 SHAP-Enhanced XGBoost Model for Interpretable Prediction of Coseismic Landslides
Haijia Wen, Bo Liu, Mingrui Di, et al.
Advances in Space Research (2024) Vol. 74, Iss. 8, pp. 3826-3854
Closed Access | Times Cited: 9

Showing 9 citing articles:

Quantitative assessment of the GLOF risk along China-Nepal transboundary basins by integrating remote sensing, machine learning, and hydrodynamic model
Manish Raj Gouli, Kaiheng Hu, Nitesh Khadka, et al.
International Journal of Disaster Risk Reduction (2025), pp. 105231-105231
Closed Access | Times Cited: 4
Interpretable Combinatorial Machine Learning-Based Shale Fracability Evaluation Methods
Di Wang, Dingyu Jiao, Zihang Zhang, et al.
Energies (2025) Vol. 18, Iss. 1, pp. 186-186
Open Access | Times Cited: 1
Enhancing Air Conditioning System Efficiency Through Load Prediction and Deep Reinforcement Learning: A Case Study of Ground Source Heat Pumps
Zhitao Wang, Yubin Qiu, Shiyu Zhou, et al.
Energies (2025) Vol. 18, Iss. 1, pp. 199-199
Open Access | Times Cited: 1
Deep learning model based on coupled SWAT and interpretable methods for water quality prediction under the influence of non-point source pollution
Juan Huan, Yixiong Fan, Xiangen Xu, et al.
Computers and Electronics in Agriculture (2025) Vol. 231, pp. 109985-109985
Closed Access | Times Cited: 1
On Robustness of the Explanatory Power of Machine Learning Models: Insights From a New Explainable AI Approach Using Sensitivity Analysis
Banamali Panigrahi, Saman Razavi, Lorne E. Doig, et al.
Water Resources Research (2025) Vol. 61, Iss. 3
Open Access | Times Cited: 1
Assessment of Water Hydrochemical Parameters Using Machine Learning Tools
Ivan Malashin, Vladimir Nelyub, А. С. Бородулин, et al.
Sustainability (2025) Vol. 17, Iss. 2, pp. 497-497
Open Access
Driving mechanisms and threshold identification of landscape ecological risk: A nonlinear perspective from the Qilian Mountains, China
Bin Qiao, Hao Yang, Xiaoyun Cao, et al.
Ecological Indicators (2025) Vol. 173, pp. 113342-113342
Closed Access
Can different machine learning methods have consistent interpretations of DEM-based factors in shallow landslide susceptibility assessments?
Fanshu Xu, Qiang Xu, Chuanhao Pu, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2025)
Open Access
Coseismic landslides caused by the 2022 Luding earthquake in China: insights from remote sensing interpretations and machine learning models
Zhiwen Ding, Chun Wang
Frontiers in Earth Science (2025) Vol. 13
Open Access
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