Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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 Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping
Zhu Liang, Changming Wang, Zhijie Duan, et al.
Remote Sensing (2021) Vol. 13, Iss. 8, pp. 1464-1464
Open Access | Times Cited: 36

Showing 1-25 of 36 citing articles:

Landslide Susceptibility Mapping Using Machine Learning: A Literature Survey
Moziihrii Ado, Khwairakpam Amitab, Arnab Kumar Maji, et al.
Remote Sensing (2022) Vol. 14, Iss. 13, pp. 3029-3029
Open Access | Times Cited: 109
Review of landslide susceptibility assessment based on knowledge mapping
Chen Yong, Jinlong Dong, Fei Guo, et al.
Stochastic Environmental Research and Risk Assessment (2022) Vol. 36, Iss. 9, pp. 2399-2417
Closed Access | Times Cited: 96
Refined and dynamic susceptibility assessment of landslides using InSAR and machine learning models
Yingdong Wei, Haijun Qiu, Zijing Liu, et al.
Geoscience Frontiers (2024) Vol. 15, Iss. 6, pp. 101890-101890
Open Access | Times Cited: 44
Landslide susceptibility assessment using information quantity and machine learning integrated models: a case study of Sichuan province, southwestern China
Pengtao Zhao, Ying Wang, Yi Xie, et al.
Earth Science Informatics (2025) Vol. 18, Iss. 2
Closed Access | Times Cited: 3
Landslide and Wildfire Susceptibility Assessment in Southeast Asia Using Ensemble Machine Learning Methods
Qian He, Ziyu Jiang, Ming Wang, et al.
Remote Sensing (2021) Vol. 13, Iss. 8, pp. 1572-1572
Open Access | Times Cited: 64
Investigation of steel frame damage based on computer vision and deep learning
Bubryur Kim, N. Yuvaraj, Hee Won Park, et al.
Automation in Construction (2021) Vol. 132, pp. 103941-103941
Closed Access | Times Cited: 58
Slope-Unit Scale Landslide Susceptibility Mapping Based on the Random Forest Model in Deep Valley Areas
Hui Deng, Xiantan Wu, Wenjiang Zhang, et al.
Remote Sensing (2022) Vol. 14, Iss. 17, pp. 4245-4245
Open Access | Times Cited: 51
Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies
Faming Huang, Siyu Tao, Deying Li, et al.
Remote Sensing (2022) Vol. 14, Iss. 18, pp. 4436-4436
Open Access | Times Cited: 51
Multi-hazard spatial modeling via ensembles of machine learning and meta-heuristic techniques
Mojgan Bordbar, Hossein Aghamohammadi, Hamid Reza Pourghasemi, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 42
Landslide Susceptibility Mapping and Driving Mechanisms in a Vulnerable Region Based on Multiple Machine Learning Models
Haiwei Yu, Wenjie Pei, Jingyi Zhang, et al.
Remote Sensing (2023) Vol. 15, Iss. 7, pp. 1886-1886
Open Access | Times Cited: 30
Unsupervised Learning - A Systematic Literature Review
Salim Dridi
(2024)
Open Access | Times Cited: 16
Prediction of spatial landslide susceptibility applying the novel ensembles of CNN, GLM and random forest in the Indian Himalayan region
Sunil Saha, Anik Saha, Tusar Kanti Hembram, et al.
Stochastic Environmental Research and Risk Assessment (2022) Vol. 36, Iss. 10, pp. 3597-3616
Closed Access | Times Cited: 32
A bibliometric and content analysis of research trends on GIS-based landslide susceptibility from 2001 to 2020
Junpeng Huang, Xiyong Wu, Sixiang Ling, et al.
Environmental Science and Pollution Research (2022) Vol. 29, Iss. 58, pp. 86954-86993
Closed Access | Times Cited: 23
Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China
Baoxin Zhao, Jingzhong Zhu, Youbiao Hu, et al.
Geofluids (2022) Vol. 2022, pp. 1-32
Open Access | Times Cited: 17
Autonomous Defending Approaches Based on Machine Learning for Android Malware Detection
Khalid Alemerien, Mohammad Almseidin, Enshirah Altarawneh, et al.
Advances in computational intelligence and robotics book series (2025), pp. 325-374
Closed Access
Assessment of Landslide Susceptibility Using the PCA and ANFIS with Various Metaheuristic Algorithms
Zelu Chen, Hechun Quan, Ri Jin, et al.
KSCE Journal of Civil Engineering (2024) Vol. 28, Iss. 4, pp. 1461-1474
Open Access | Times Cited: 3
Review of the Accuracy of Satellite Remote Sensing Techniques in Identifying Coastal Aquaculture Facilities
Ao Chen, Zehua Lv, Junbo Zhang, et al.
Fishes (2024) Vol. 9, Iss. 2, pp. 52-52
Open Access | Times Cited: 3
Quantitative evaluation of uncertainty and interpretability in machine learning-based landslide susceptibility mapping through feature selection and explainable AI
Xuan-Hien Le, Chanul Choi, Song Eu, et al.
Frontiers in Environmental Science (2024) Vol. 12
Open Access | Times Cited: 3
Comparison of optimized data-driven models for landslide susceptibility mapping
Armin Ghayur Sadigh, Ali Asghar Alesheikh, Sayed M. Bateni, et al.
Environment Development and Sustainability (2023) Vol. 26, Iss. 6, pp. 14665-14692
Closed Access | Times Cited: 8
Exploration and Comparison of the Effect of Conventional and Advanced Modeling Algorithms on Landslide Susceptibility Prediction: A Case Study from Yadong Country, Tibet
Zhu Liang, Weiping Peng, Wei Liu, et al.
Applied Sciences (2023) Vol. 13, Iss. 12, pp. 7276-7276
Open Access | Times Cited: 8
Evaluating the Impact of Recursive Feature Elimination on Machine Learning Models for Predicting Forest Fire-Prone Zones
Ali Rezaei Barzani, Parham Pahlavani, Omid Ghorbanzadeh, et al.
Fire (2024) Vol. 7, Iss. 12, pp. 440-440
Open Access | Times Cited: 2
基于知识图谱的滑坡易发性评价文献综述及研究进展
Fei Guo, Lai Peng, Faming Huang, et al.
Earth Science-Journal of China University of Geosciences (2024) Vol. 49, Iss. 5, pp. 1584-1584
Open Access | Times Cited: 2
A Systematic review of machine learning based landslide susceptibility mapping
Semachew Molla Kassa
Put i saobraćaj (2024) Vol. 70, Iss. 2, pp. 23-30
Open Access | Times Cited: 2
A Research on Cross-Regional Debris Flow Susceptibility Mapping Based on Transfer Learning
Ruiyuan Gao, Changming Wang, Songling Han, et al.
Remote Sensing (2022) Vol. 14, Iss. 19, pp. 4829-4829
Open Access | Times Cited: 10
Global research trends in seismic landslide: A bibliometric analysis
Mengjie Yang, Shenghua Cui, Tao Jiang
Earthquake research advances (2024) Vol. 5, Iss. 1, pp. 100329-100329
Open Access | Times Cited: 1
Page 1 - Next Page

Scroll to top