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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:

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

Showing 23 citing articles:

Ground-motion simulations using two-dimensional convolution condition adversarial neural network(2D-cGAN)
Yanwen Huang, Cheng Yang, Xiaodan Sun, et al.
Soil Dynamics and Earthquake Engineering (2024) Vol. 178, pp. 108444-108444
Closed Access | Times Cited: 10
Machine learning and soft voting ensemble classification for earthquake induced damage to bridges
Dipendra Gautam, Ankit Bhattarai, Rajesh Rupakhety
Engineering Structures (2024) Vol. 303, pp. 117534-117534
Closed Access | Times Cited: 7
Development of a framework for the prediction of slope stability using machine learning paradigms
K. C. Rajan, Milan Aryal, Keshab Sharma, et al.
Natural Hazards (2024)
Closed Access | Times Cited: 7
Enhancing co-seismic landslide susceptibility, building exposure, and risk analysis through machine learning
Ajaya Pyakurel, Diwakar K.C., Bhim Kumar Dahal
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 6
Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway)
Nicola Nocentini, Ascanio Rosi, Luca Piciullo, et al.
Landslides (2024)
Open Access | Times Cited: 6
Integrating dynamic factors for predicting future landslide susceptibility
Suraj Lamichhane, Arhat Ratna Kansakar, Nirajan Devkota, et al.
Environmental Earth Sciences (2025) Vol. 84, Iss. 3
Closed Access
Classification of geogrid reinforcement in aggregate using machine learning techniques
Samuel Olamide Aregbesola, Yong‐Hoon Byun
International Journal of Geo-Engineering (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 4
Examining the Influence of Different Inventories on Shallow Landslide Susceptibility Modeling: An Assessment Using Machine Learning and Statistical Approaches
Helen Cristina Dias, Daniel Hölbling, Carlos Henrique Grohmann
Geosciences (2025) Vol. 15, Iss. 3, pp. 77-77
Open Access
EFFECTIVENESS OF MACHINE LEARNING METHODS IN DETERMINING EARTHQUAKE PROBABLE AREAS: EXAMPLE OF KAZAKHSTAN
Gulnur Kazbekova, Arypzhan Aben, Anuarbek Amanov, et al.
Scientific Journal of Astana IT University (2025)
Open Access
Forecasting landslide deformation by integrating domain knowledge into interpretable deep learning considering spatiotemporal correlations
Zhengjing Ma, Gang Mei
Journal of Rock Mechanics and Geotechnical Engineering (2024)
Open Access | Times Cited: 3
Stacking Ensemble-Based Machine Learning Model for Predicting Deterioration Components of Steel W-Section Beams
Azadeh Khoshkroodi, Hossein Parvini Sani, Mojtaba Aajami
Buildings (2024) Vol. 14, Iss. 1, pp. 240-240
Open Access | Times Cited: 2
Investigation of Landslide Susceptibility Decision Mechanisms in Different Ensemble-Based Machine Learning Models with Various Types of Factor Data
Jiakai Lu, Chao Ren, Weiting Yue, et al.
Sustainability (2023) Vol. 15, Iss. 18, pp. 13563-13563
Open Access | Times Cited: 6
Nonlinear tree based regression ensemble modeling for repair cost prediction in earthquake damaged RC bridges
Dipendra Gautam, Rajesh Rupakhety
Soil Dynamics and Earthquake Engineering (2024) Vol. 187, pp. 108947-108947
Closed Access | Times Cited: 1
Co-seismic landslide susceptibility mapping for the Luding earthquake area based on heterogeneous ensemble machine learning models
Rui Zhang, Yunjie Yang, Tianyu Wang, et al.
International Journal of Digital Earth (2024) Vol. 17, Iss. 1
Open Access | Times Cited: 1
Application of ML- based approach for co-seismic landslides susceptibility mapping and identification of important controlling factors in eastern Himalayan region
Saurav Kumar, Aniruddha Sengupta
Environmental Earth Sciences (2024) Vol. 83, Iss. 21
Closed Access | Times Cited: 1
VERE Py-framework: Dual environment for physically-informed machine learning in seismic landslide hazard mapping driven by InSAR.
Gerardo Grelle, Luigi Guerriero, Domenico Calcaterra, et al.
Environmental Modelling & Software (2024), pp. 106287-106287
Closed Access | Times Cited: 1
Assessment of landslide susceptibility along the Lanzhou-xinjiang high-speed railway: A case study of Menyuan-Shandanmachang
Jiao Li, Zhiwei Zhou, Wei Ma
Transportation Geotechnics (2024), pp. 101473-101473
Closed Access | Times Cited: 1
A novel technology for unraveling the spatial risk of Natech disasters based on machine learning and GIS: a case study from the city of Changzhou, China
Weiyi Ju, Zhixiang Xing
Earth Science Informatics (2024)
Closed Access
Artificial intelligence for assessing the planets' positions as a precursor to earthquake events
Tarik El Moudden, Mohamed Amnai, Ali Choukri, et al.
Journal of Geodynamics (2024), pp. 102057-102057
Closed Access
AI and Machine Learning in Earthquake Prediction
U. V. Anbazhagu, R. Sonia, Rakesh Kumar Grover, et al.
Advances in computer and electrical engineering book series (2024), pp. 1-32
Closed Access
Automated signal‐based evaluation of dynamic cone resistance via machine learning for subsurface characterization
Samuel Olamide Aregbesola, Yong‐Hoon Byun
Computer-Aided Civil and Infrastructure Engineering (2024) Vol. 39, Iss. 16, pp. 2541-2552
Open Access
Landslide susceptibility prediction method based on HSOM and IABPA-CNN in Wenchuan earthquake disaster area
Dunlong Liu, Qian Xiang, Xuejia Sang, et al.
Journal of Mountain Science (2024) Vol. 21, Iss. 12, pp. 4001-4018
Closed Access
Earthquake prognosis using machine learning
Sachin Sawantt, Purva Golegaonkar, Prayas Gondane, et al.
ITM Web of Conferences (2023) Vol. 56, pp. 05017-05017
Open Access | Times Cited: 1
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