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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 analyses using Random Forest, C4.5, and C5.0 with balanced and unbalanced datasets
Burak F. Tanyu, Aiyoub Abbaspour, Yashar Alimohammadlou, et al.
CATENA (2021) Vol. 203, pp. 105355-105355
Closed Access | Times Cited: 101

Showing 1-25 of 101 citing articles:

Machine learning and landslide studies: recent advances and applications
Faraz S. Tehrani, Michele Calvello, Zhongqiang Liu, et al.
Natural Hazards (2022) Vol. 114, Iss. 2, pp. 1197-1245
Open Access | Times Cited: 149
Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models
Jitendra Khatti, Kamaldeep Singh Grover
Journal of Rock Mechanics and Geotechnical Engineering (2023) Vol. 15, Iss. 11, pp. 3010-3038
Open Access | Times Cited: 66
Biochar implications for the engineering properties of soils: A review
Yu Lu, Kai Gu, Zhengtao Shen, et al.
The Science of The Total Environment (2023) Vol. 888, pp. 164185-164185
Closed Access | Times Cited: 48
Advanced integration of ensemble learning and MT-InSAR for enhanced slow-moving landslide susceptibility zoning
Taorui Zeng, Liyang Wu, Yuichi S. Hayakawa, et al.
Engineering Geology (2024) Vol. 331, pp. 107436-107436
Closed Access | Times Cited: 25
PSLSA v2.0: An automatic Python package integrating machine learning models for regional landslide susceptibility assessment
Zizheng Guo, Haojie Wang, Jun He, et al.
Environmental Modelling & Software (2025) Vol. 186, pp. 106367-106367
Closed Access | Times Cited: 5
A Robust Deep-Learning Model for Landslide Susceptibility Mapping: A Case Study of Kurdistan Province, Iran
Bahareh Ghasemian, Himan Shahabi, Ataollah Shirzadi, et al.
Sensors (2022) Vol. 22, Iss. 4, pp. 1573-1573
Open Access | Times Cited: 44
Prediction of compaction parameters of compacted soil using LSSVM, LSTM, LSBoostRF, and ANN
Jitendra Khatti, Kamaldeep Singh Grover
Innovative Infrastructure Solutions (2023) Vol. 8, Iss. 2
Closed Access | Times Cited: 41
An Ensemble Approach of Feature Selection and Machine Learning Models for Regional Landslide Susceptibility Mapping in the Arid Mountainous Terrain of Southern Peru
Chandan Kumar, Gabriel Walton, Paul M. Santi, et al.
Remote Sensing (2023) Vol. 15, Iss. 5, pp. 1376-1376
Open Access | Times Cited: 31
Spatial Prediction of Wildfire Susceptibility Using Hybrid Machine Learning Models Based on Support Vector Regression in Sydney, Australia
Arip Syaripudin Nur, Yong Je Kim, Joon Lee, et al.
Remote Sensing (2023) Vol. 15, Iss. 3, pp. 760-760
Open Access | Times Cited: 29
Comparison of LiDAR- and UAV-derived data for landslide susceptibility mapping using Random Forest algorithm
Felicia França Pereira, Tatiana Sussel Gonçalves Mendes, Silvio Jorge Coelho Simões, et al.
Landslides (2023) Vol. 20, Iss. 3, pp. 579-600
Closed Access | Times Cited: 23
Selection of contributing factors for predicting landslide susceptibility using machine learning and deep learning models
Cheng Chen, Lei Fan
Stochastic Environmental Research and Risk Assessment (2023)
Open Access | Times Cited: 23
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
Random Cross-Validation Produces Biased Assessment of Machine Learning Performance in Regional Landslide Susceptibility Prediction
Chandan Kumar, Gabriel Walton, Paul M. Santi, et al.
Remote Sensing (2025) Vol. 17, Iss. 2, pp. 213-213
Open Access | Times Cited: 1
Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification
Ding Xia, Huiming Tang, Sixuan Sun, et al.
Remote Sensing (2022) Vol. 14, Iss. 11, pp. 2707-2707
Open Access | Times Cited: 35
Mapping Landslide Susceptibility Over Large Regions With Limited Data
Jacob Woodard, Benjamin B. Mirus, Matthew M. Crawford, et al.
Journal of Geophysical Research Earth Surface (2023) Vol. 128, Iss. 5
Open Access | Times Cited: 22
Spatio-temporal landslide forecasting using process-based and data-driven approaches: A case study from Western Ghats, India
Minu Treesa Abraham, Manjunath Vaddapally, Neelima Satyam, et al.
CATENA (2023) Vol. 223, pp. 106948-106948
Closed Access | Times Cited: 18
Landslide susceptibility assessment using locally weighted learning integrated with machine learning algorithms
Haoyuan Hong
Expert Systems with Applications (2023) Vol. 237, pp. 121678-121678
Closed Access | Times Cited: 17
Feature adaptation for landslide susceptibility assessment in “no sample” areas
Yan Su, Y. Chen, Xiaohe Lai, et al.
Gondwana Research (2024) Vol. 131, pp. 1-17
Closed Access | Times Cited: 7
Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique
Chao Zhou, Yue Wang, Ying Cao, et al.
Geocarto International (2024) Vol. 39, Iss. 1
Open Access | Times Cited: 7
Enhancing the Performance of Machine Learning and Deep Learning-Based Flood Susceptibility Models by Integrating Grey Wolf Optimizer (GWO) Algorithm
Ali Nouh Mabdeh, R. S. Ajin, Seyed Vahid Razavi-Termeh, et al.
Remote Sensing (2024) Vol. 16, Iss. 14, pp. 2595-2595
Open Access | Times Cited: 7
Shared Blocks-Based Ensemble Deep Learning for Shallow Landslide Susceptibility Mapping
Taşkın Kavzoǧlu, Alihan Teke, Elif Özlem Yılmaz
Remote Sensing (2021) Vol. 13, Iss. 23, pp. 4776-4776
Open Access | Times Cited: 37
A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides
Biswajeet Pradhan, Maher Ibrahim Sameen, Husam A. H. Al-Najjar, et al.
Remote Sensing (2021) Vol. 13, Iss. 22, pp. 4521-4521
Open Access | Times Cited: 36
Characterizing the Distribution Pattern and a Physically Based Susceptibility Assessment of Shallow Landslides Triggered by the 2019 Heavy Rainfall Event in Longchuan County, Guangdong Province, China
Siyuan Ma, Xiaoyi Shao, Chong Xu
Remote Sensing (2022) Vol. 14, Iss. 17, pp. 4257-4257
Open Access | Times Cited: 28
Performance evaluation of machine learning and statistical techniques for modelling landslide susceptibility with limited field data
A.L. Achu, Jobin Thomas, C. D. Aju, et al.
Earth Science Informatics (2022) Vol. 16, Iss. 1, pp. 1025-1039
Closed Access | Times Cited: 27
Spatiotemporal assessment of landslide susceptibility in Southern Sichuan, China using SA-DBN, PSO-DBN and SSA-DBN models compared with DBN model
Jiaying Li, Weidong Wang, Guangqi Chen, et al.
Advances in Space Research (2022) Vol. 69, Iss. 8, pp. 3071-3087
Closed Access | Times Cited: 23
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