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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:

Evaluating Landslide Susceptibility Using Sampling Methodology and Multiple Machine Learning Models
Yingze Song, Degang Yang, Weicheng Wu, et al.
ISPRS International Journal of Geo-Information (2023) Vol. 12, Iss. 5, pp. 197-197
Open Access | Times Cited: 18

Showing 18 citing articles:

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: 26
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: 13
Comparative analysis of the TabNet algorithm and traditional machine learning algorithms for landslide susceptibility assessment in the Wanzhou Region of China
Song Yingze, Song Yingxu, Xin Zhang, et al.
Natural Hazards (2024) Vol. 120, Iss. 8, pp. 7627-7652
Closed Access | Times Cited: 10
Identifying potential debris flow hazards after the 2022 Mw 6.8 luding earthquake in southwestern China
Ming Chen, Ming Chang, Qiang Xu, et al.
Bulletin of Engineering Geology and the Environment (2024) Vol. 83, Iss. 6
Closed Access | Times Cited: 6
Evaluation of Rainfall-Induced Accumulation Landslide Susceptibility Based on Remote Sensing Interpretation
Zhen Wu, Runqing Ye, Jue Huang, et al.
Remote Sensing (2025) Vol. 17, Iss. 2, pp. 339-339
Open Access
Landslide susceptibility assessment through multi-model stacking and meta-learning in Poyang County, China
Yong Song, Yingxu Song, Chengnan Wang, et al.
Geomatics Natural Hazards and Risk (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 2
Integrating ensemble machine learning and explainable AI for enhanced forest fire susceptibility analysis and risk assessment in Türkiye’s Mediterranean region
Hasan Tonbul
Earth Science Informatics (2024)
Closed Access | Times Cited: 2
Investigating the Effects of Different Data Classification Methods on Landslide Susceptibility Mapping
Halil Akıncı, Ayşe Yavuz Özalp
Advances in Space Research (2024)
Closed Access | Times Cited: 2
Comparison between Machine Learning and Physical Models Applied to the Evaluation of Co-Seismic Landslide Hazard
José Carlos Román-Herrera, Martín Jesús Rodríguez-Peces, Julio Garzón-Roca
Applied Sciences (2023) Vol. 13, Iss. 14, pp. 8285-8285
Open Access | Times Cited: 5
A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques. Case Study: Northern Lima Commonwealth, Peru
Edwin Noe Badillo-Rivera, Manuel Daniel Olcese Huerta, Ramiro Santiago Chirinos, et al.
(2024)
Open Access | Times Cited: 1
Important considerations in machine learning-based landslide susceptibility assessment under future climate conditions
Yi Han, Shabnam J. Semnani
Acta Geotechnica (2024)
Open Access | Times Cited: 1
A Comprehensive Comparison of Stable and Unstable Area Sampling Strategies in Large-Scale Landslide Susceptibility Models Using Machine Learning Methods
Marko Sinčić, Sanja Bernat Gazibara, Mauro Rossi, et al.
Remote Sensing (2024) Vol. 16, Iss. 16, pp. 2923-2923
Open Access | Times Cited: 1
An Integrated Framework of Positive-Unlabeled and Imbalanced Learning for Landslide Susceptibility Mapping
Zijin Fu, Hao Ma, Fawu Wang, et al.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2024) Vol. 17, pp. 15596-15611
Open Access | Times Cited: 1
Investigating landslide data balancing for susceptibility mapping using generative and machine learning models
Yuhang Jiang, Wei Wang, Lifang Zou, et al.
Landslides (2024)
Closed Access | Times Cited: 1
Exploring class imbalance with under-sampling, over-sampling, and hybrid sampling based on Mahalanobis distance for landslide susceptibility assessment: a case study of the 2018 Iburi earthquake induced landslides in Hokkaido, Japan
Kounghoon Nam, Jongtae Kim, Byung‐Gon Chae
Geosciences Journal (2023) Vol. 28, Iss. 1, pp. 71-94
Closed Access | Times Cited: 2
A Comparative Study of Susceptibility and Hazard for Mass Movements Applying Quantitative Machine Learning Techniques—Case Study: Northern Lima Commonwealth, Peru
Edwin Badillo-Rivera, Manuel Olcese, Ramiro Santiago, et al.
Geosciences (2024) Vol. 14, Iss. 6, pp. 168-168
Open Access
Algorithmic Insights into Mapping Landslide Susceptibility: A Random Forest Approach in High-Risk Zones of Uttarakhand
Varsha Bhosale, Omkar Kadam, Hrishikesh Pendurkar, et al.
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2024) Vol. 35, pp. 10197-10202
Closed Access
Methods of Cluster Analysis for Detection of Uniform Displacement Zones of Landslides and Anti-Landslide Structures
Khailak Andrii, Shults Roman
Journal of Basic & Applied Sciences (2023) Vol. 19, pp. 151-162
Open Access
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