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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:

Landslide Susceptibility Modeling Using Remote Sensing Data and Random SubSpace-Based Functional Tree Classifier
Tao Peng, Yunzhi Chen, Wei Chen
Remote Sensing (2022) Vol. 14, Iss. 19, pp. 4803-4803
Open Access | Times Cited: 31

Showing 1-25 of 31 citing articles:

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
Optimization of SVR and CatBoost models using metaheuristic algorithms to assess landslide susceptibility
R. S. Ajin, Samuele Segoni, Riccardo Fanti
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 11
Application of Artificial Intelligence and Remote Sensing for Landslide Detection and Prediction: Systematic Review
Stephen Akosah, Ivan Gratchev, Donghyun Kim, et al.
Remote Sensing (2024) Vol. 16, Iss. 16, pp. 2947-2947
Open Access | Times Cited: 9
Assessment of the effects of characterization methods selection on the landslide susceptibility: a comparison between logistic regression (LR), naive bayes (NB) and radial basis function network (RBF Network)
Hui Shang, Lixiang Su, Yang Liu, et al.
Bulletin of Engineering Geology and the Environment (2025) Vol. 84, Iss. 3
Closed Access | Times Cited: 1
Spatial Prediction of Landslide Susceptibility Using Logistic Regression (LR), Functional Trees (FTs), and Random Subspace Functional Trees (RSFTs) for Pengyang County, China
Hui Shang, Lixiang Su, Wei Chen, et al.
Remote Sensing (2023) Vol. 15, Iss. 20, pp. 4952-4952
Open Access | Times Cited: 16
GIS-Based Landslide Susceptibility Modeling: A Comparison between Best-First Decision Tree and Its Two Ensembles (BagBFT and RFBFT)
Jingyun Gui, Leandro R. Alejano, Miao Yao, et al.
Remote Sensing (2023) Vol. 15, Iss. 4, pp. 1007-1007
Open Access | Times Cited: 14
Hybrid Integration of Bagging and Decision Tree Algorithms for Landslide Susceptibility Mapping
Qi Zhang, Zixin Ning, Xiaohu Ding, et al.
Water (2024) Vol. 16, Iss. 5, pp. 657-657
Open Access | Times Cited: 5
A Combination of Deep Autoencoder and Multi-Scale Residual Network for Landslide Susceptibility Evaluation
Zhuolu Wang, Shenghua Xu, Jiping Liu, et al.
Remote Sensing (2023) Vol. 15, Iss. 3, pp. 653-653
Open Access | Times Cited: 13
Landslide susceptibility assessment of the Asi watershed, southern Türkiye
Senem Tekin, Adolfo Quesada‐Román, Tolga Çan
TURKISH JOURNAL OF EARTH SCIENCES (2024) Vol. 33, Iss. 2, pp. 208-223
Open Access | Times Cited: 2
Land Use and Land Cover as a Conditioning Factor in Landslide Susceptibility: A Literature Review
Vipin Upadhyay, Himisha Dixit
Advances in natural and technological hazards research (2024), pp. 431-458
Closed Access | Times Cited: 2
Predicting landslide and debris flow susceptibility using Logitboost alternating decision trees and ensemble techniques
Cong Quan Nguyen, Anh Nguyen, Hieu Tran, et al.
Natural Hazards (2024)
Closed Access | Times Cited: 2
Credal-Decision-Tree-Based Ensembles for Spatial Prediction of Landslides
Jingyun Gui, Ignacio Pérez‐Rey, Miao Yao, et al.
Water (2023) Vol. 15, Iss. 3, pp. 605-605
Open Access | Times Cited: 6
Improving the Understanding of Landslide Development in Alpine Forest Regions Using the InSAR Technique: A Case Study in Xiaojin County China
Shu Zhou, Zhen Guo, Gang Huang, et al.
Applied Sciences (2023) Vol. 13, Iss. 21, pp. 11851-11851
Open Access | Times Cited: 4
Predicting gully formation: An approach for assessing susceptibility and future risk
Leila Goli Mokhtari, Nadiya Baghaei Nejad, Aliasghar Beheshti
Natural Resource Modeling (2024)
Open Access | Times Cited: 1
Diagnosis of Surface Defects in Hot-Rolled Steel from Deep Learning Features Using Machine Learning Algorithms
Sriraman Madhavan, Naveen Venkatesh Sridharan, Tapan K. Mahanta, et al.
Arabian Journal for Science and Engineering (2024)
Closed Access | Times Cited: 1
Landslide Susceptibility Modeling Using a Deep Random Neural Network
Cheng Huang, Fang Li, Lei Wei, et al.
Applied Sciences (2022) Vol. 12, Iss. 24, pp. 12887-12887
Open Access | Times Cited: 6
Local perception and adaptation strategies to landslide occurrence in the Kivu catchment of Rwanda
Ma-Lyse Nema, Bachir Saley Mahaman, Arona Diédhiou, et al.
(2023)
Open Access | Times Cited: 1
An integrated approach for gully erosion susceptibility mapping and factor effect analysis
Jingge Liu, Alireza Arabameri, Chandan Surabhi Das, et al.
Advances in Space Research (2024)
Closed Access
Landslide susceptibility assessment for the Darjeeling Toy Train route: a GIS and machine learning approach
Prasanya Sarkar, Madhumita Mondal, Alok Sarkar, et al.
Stochastic Environmental Research and Risk Assessment (2024)
Closed Access
Comment on nhess-2023-47
Ma-Lyse Nema, Bachir Saley Mahaman, Arona Diédhiou, et al.
(2023)
Open Access
Comment on nhess-2023-47
Ma-Lyse Nema, Bachir Saley Mahaman, Arona Diédhiou, et al.
(2023)
Open Access
Comment on nhess-2023-47
Ma-Lyse Nema, Bachir Saley Mahaman, Arona Diédhiou, et al.
(2023)
Open Access
Comment on nhess-2023-47
Uğur Öztürk
(2023)
Open Access
Comment on nhess-2023-47
Ma-Lyse Nema, Bachir Saley Mahaman, Arona Diédhiou, et al.
(2023)
Open Access
Reply on RC1
Ma-Lyse Nema
(2023)
Open Access
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