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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:

Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model
Junyi Zhang, Xianglong Ma, Jialan Zhang, et al.
Journal of Environmental Management (2023) Vol. 332, pp. 117357-117357
Closed Access | Times Cited: 232

Showing 1-25 of 232 citing articles:

Explainable artificial intelligence in disaster risk management: Achievements and prospective futures
Saman Ghaffarian, Firouzeh Taghikhah, Holger R. Maier
International Journal of Disaster Risk Reduction (2023) Vol. 98, pp. 104123-104123
Open Access | Times Cited: 57
Modelling landslide susceptibility prediction: A review and construction of semi-supervised imbalanced theory
Faming Huang, Haowen Xiong, Shui‐Hua Jiang, et al.
Earth-Science Reviews (2024) Vol. 250, pp. 104700-104700
Closed Access | Times Cited: 57
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: 51
A systematic review of trustworthy artificial intelligence applications in natural disasters
A. S. Albahri, Yahya Layth Khaleel, Mustafa Abdulfattah Habeeb, et al.
Computers & Electrical Engineering (2024) Vol. 118, pp. 109409-109409
Open Access | Times Cited: 42
Physics-informed optimization for a data-driven approach in landslide susceptibility evaluation
Songlin Liu, Luqi Wang, Wengang Zhang, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2024) Vol. 16, Iss. 8, pp. 3192-3205
Open Access | Times Cited: 28
Multimodal Machine Learning Guides Low Carbon Aeration Strategies in Urban Wastewater Treatment
Hongcheng Wang, Yuqi Wang, Xu Wang, et al.
Engineering (2024) Vol. 36, pp. 51-62
Open Access | Times Cited: 26
Optimizing landslide susceptibility mapping using machine learning and geospatial techniques
Gazali Agboola, Leila Hashemi-Beni, Tamer Elbayoumi, et al.
Ecological Informatics (2024) Vol. 81, pp. 102583-102583
Open Access | Times Cited: 25
Deep Learning for Exploring Landslides with Remote Sensing and Geo-Environmental Data: Frameworks, Progress, Challenges, and Opportunities
Qi Zhang, Teng Wang
Remote Sensing (2024) Vol. 16, Iss. 8, pp. 1344-1344
Open Access | Times Cited: 24
On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values
Nan Wang, Hongyan Zhang, Ashok Dahal, et al.
Geoscience Frontiers (2024) Vol. 15, Iss. 4, pp. 101800-101800
Open Access | Times Cited: 22
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: 17
A coupled extreme gradient boosting-MPA approach for estimating daily reference evapotranspiration
Mohammed Achite, Hamid Nasiri, Okan Mert Katipoğlu, et al.
Theoretical and Applied Climatology (2025) Vol. 156, Iss. 2
Closed Access | Times Cited: 3
Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer
Yuqi Wang, Hongcheng Wang, Yunpeng Song, et al.
Water Research (2023) Vol. 246, pp. 120676-120676
Closed Access | Times Cited: 41
A hybrid machine learning model for landslide-oriented risk assessment of long-distance pipelines
Haijia Wen, Lei Liu, Jialan Zhang, et al.
Journal of Environmental Management (2023) Vol. 342, pp. 118177-118177
Closed Access | Times Cited: 35
Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation
Deliang Sun, D C Chen, Jialan Zhang, et al.
Land (2023) Vol. 12, Iss. 5, pp. 1018-1018
Open Access | Times Cited: 34
AI explainability framework for environmental management research
Mehrdad Arashpour
Journal of Environmental Management (2023) Vol. 342, pp. 118149-118149
Open Access | Times Cited: 25
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: 24
Modeling landslide susceptibility based on convolutional neural network coupling with metaheuristic optimization algorithms
Zhuo Chen, Danqing Song
International Journal of Digital Earth (2023) Vol. 16, Iss. 1, pp. 3384-3416
Open Access | Times Cited: 23
Selection of contributing factors for predicting landslide susceptibility using machine learning and deep learning models
Cheng Chen, Lei Fan
Stochastic Environmental Research and Risk Assessment (2023)
Open Access | Times Cited: 23
Deciphering decision-making mechanisms for the susceptibility of different slope geohazards: A case study on a SMOTE-RF-SHAP hybrid model
Junhao Huang, Haijia Wen, Jiwei Hu, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2024)
Open Access | Times Cited: 16
An interpretable model for landslide susceptibility assessment based on Optuna hyperparameter optimization and Random Forest
Xin Xiao, Yi Zou, Jiangcheng Huang, et al.
Geomatics Natural Hazards and Risk (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 15
Ecological Security Pattern based on XGBoost-MCR model: A case study of the Three Gorges Reservoir Region
Deliang Sun, Xiaoqing Wu, Haijia Wen, et al.
Journal of Cleaner Production (2024) Vol. 470, pp. 143252-143252
Closed Access | Times Cited: 15
A new approach for predicting oil mobilities and unveiling their controlling factors in a lacustrine shale system: Insights from interpretable machine learning model
Enze Wang, Yingxiao Fu, Tonglou Guo, et al.
Fuel (2024) Vol. 379, pp. 132958-132958
Closed Access | Times Cited: 15
Effects of patterns of urban green-blue landscape on carbon sequestration using XGBoost-SHAP model
Yangyang Yuan, Wei Guo, Siqi Tang, et al.
Journal of Cleaner Production (2024), pp. 143640-143640
Closed Access | Times Cited: 15
Exploring the association between the built environment and positive sentiments of tourists in traditional villages in Fuzhou, China
Zhengyan Chen, Honghui Yang, Yishan Lin, et al.
Ecological Informatics (2024) Vol. 80, pp. 102465-102465
Open Access | Times Cited: 14
GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Models
Ziqi Li
Annals of the American Association of Geographers (2024) Vol. 114, Iss. 7, pp. 1365-1385
Open Access | Times Cited: 14
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