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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 novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset
Husam A. H. Al-Najjar, Biswajeet Pradhan, Ghassan Beydoun, et al.
Gondwana Research (2022) Vol. 123, pp. 107-124
Closed Access | Times Cited: 92

Showing 1-25 of 92 citing articles:

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
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: 54
GIS-based data-driven bivariate statistical models for landslide susceptibility prediction in Upper Tista Basin, India
Jayanta Das, Pritam Saha, Rajib Mitra, et al.
Heliyon (2023) Vol. 9, Iss. 5, pp. e16186-e16186
Open Access | Times Cited: 44
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: 40
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
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: 20
Uncertainties in landslide susceptibility prediction modeling: A review on the incompleteness of landslide inventory and its influence rules
Faming Huang, Daxiong Mao, Shui‐Hua Jiang, et al.
Geoscience Frontiers (2024) Vol. 15, Iss. 6, pp. 101886-101886
Open Access | Times Cited: 17
SHAP-based insights for aerospace PHM: Temporal feature importance, dependencies, robustness, and interaction analysis
Yazan Alomari, Mátyás Andó
Results in Engineering (2024) Vol. 21, pp. 101834-101834
Open Access | Times Cited: 16
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
Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model
Junhao Wu, Zhaocai Wang, Jinghan Dong, et al.
Water Resources Research (2023) Vol. 59, Iss. 9
Closed Access | Times Cited: 33
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
Refined landslide susceptibility mapping in township area using ensemble machine learning method under dataset replenishment strategy
Fancheng Zhao, Fasheng Miao, Yiping Wu, et al.
Gondwana Research (2024) Vol. 131, pp. 20-37
Closed Access | Times Cited: 11
Interferometric Synthetic Aperture Radar (InSAR)-Based Absence Sampling for Machine-Learning-Based Landslide Susceptibility Mapping: The Three Gorges Reservoir Area, China
Ruiqi Zhang, Lele Zhang, Zhice Fang, et al.
Remote Sensing (2024) Vol. 16, Iss. 13, pp. 2394-2394
Open Access | Times Cited: 11
Landslide susceptibility mapping (LSM) based on different boosting and hyperparameter optimization algorithms: A case of Wanzhou District, China
Deliang Sun, Jing Wang, Haijia Wen, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2024) Vol. 16, Iss. 8, pp. 3221-3232
Open Access | Times Cited: 9
Exploring the spatial patterns of landslide susceptibility assessment using interpretable Shapley method: Mechanisms of landslide formation in the Sichuan-Tibet region
Jichao Lv, Rui Zhang, Age Shama, et al.
Journal of Environmental Management (2024) Vol. 366, pp. 121921-121921
Closed Access | Times Cited: 9
Enhancing long-term prediction of non-homogeneous landslides incorporating spatiotemporal graph convolutional networks and InSAR
Zongzheng Li, Jianping Chen, Chen Cao, et al.
Engineering Geology (2025), pp. 107917-107917
Closed Access | Times Cited: 1
Interpretable machine learning models and decision-making mechanisms for landslide hazard assessment under different rainfall conditions
Haijia Wen, Fangyi Yan, Junhao Huang, et al.
Expert Systems with Applications (2025), pp. 126582-126582
Closed Access | Times Cited: 1
Unraveling the evolution of landslide susceptibility: a systematic review of 30-years of strategic themes and trends
Aonan Dong, Jie Dou, Yonghu Fu, et al.
Geocarto International (2023) Vol. 38, Iss. 1
Open Access | Times Cited: 22
Hazard assessment model of ground subsidence coupling AHP, RS and GIS – A case study of Shanghai
Zhihua Zhang, Shaobin Zhang, Changtao Hu, et al.
Gondwana Research (2023) Vol. 117, pp. 344-362
Closed Access | Times Cited: 19
Novel optimized deep learning algorithms and explainable artificial intelligence for storm surge susceptibility modeling and management in a flood-prone island
Mohammed J. Alshayeb, Hoang Thi Hang, Ahmed Ali A. Shohan, et al.
Natural Hazards (2024) Vol. 120, Iss. 6, pp. 5099-5128
Closed Access | Times Cited: 8
Artificial Intelligence: A new era for spatial modelling and interpreting climate-induced hazard assessment
Abhirup Dikshit, Biswajeet Pradhan, S.S. Matin, et al.
Geoscience Frontiers (2024) Vol. 15, Iss. 4, pp. 101815-101815
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
Considering the effect of non-landslide sample selection on landslide susceptibility assessment
Youchen Zhu, Deliang Sun, Haijia Wen, et al.
Geomatics Natural Hazards and Risk (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 6
Assessing the vulnerability of Iran to subsidence hazard using a hierarchical FUCOM-GIS framework
Hamed Sadeghi, Ali Golaghaei Darzi, Behzad Voosoghi, et al.
Remote Sensing Applications Society and Environment (2023) Vol. 31, pp. 100989-100989
Closed Access | Times Cited: 15
Water depth estimation from Sentinel-2 imagery using advanced machine learning methods and explainable artificial intelligence
Vahideh Saeidi, Seyd Teymoor Seydi, Bahareh Kalantar, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 14
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