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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:

A comparison of Support Vector Machines and Bayesian algorithms for landslide susceptibility modelling
Binh Thai Pham, Indra Prakash, Khabat Khosravi, et al.
Geocarto International (2018) Vol. 34, Iss. 13, pp. 1385-1407
Closed Access | Times Cited: 107

Showing 1-25 of 107 citing articles:

Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
Quang Hung Nguyen, Haï-Bang Ly, Lanh Si Ho, et al.
Mathematical Problems in Engineering (2021) Vol. 2021, pp. 1-15
Open Access | Times Cited: 467
Improving prediction of water quality indices using novel hybrid machine-learning algorithms
Duie Tien Bui, Khabat Khosravi, John P. Tiefenbacher, et al.
The Science of The Total Environment (2020) Vol. 721, pp. 137612-137612
Closed Access | Times Cited: 322
Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran
Phuong Thao Thi Ngo, Mahdi Panahi, Khabat Khosravi, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 2, pp. 505-519
Open Access | Times Cited: 319
Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction
Binh Thai Pham, Abolfazl Jaafari, Mohammadtaghi Avand, et al.
Symmetry (2020) Vol. 12, Iss. 6, pp. 1022-1022
Open Access | Times Cited: 231
Comparative study of landslide susceptibility mapping with different recurrent neural networks
Yi Wang, Zhice Fang, Mao Wang, et al.
Computers & Geosciences (2020) Vol. 138, pp. 104445-104445
Closed Access | Times Cited: 230
Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models
Ebrahim Nohani, Meysam Moharrami, Siyamack Sharafi, et al.
Water (2019) Vol. 11, Iss. 7, pp. 1402-1402
Open Access | Times Cited: 215
Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer
Wei Chen, Xi Chen, Jianbing Peng, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 1, pp. 93-107
Open Access | Times Cited: 167
Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS: A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model
Duie Tien Bui, Khabat Khosravi, Himan Shahabi, et al.
Remote Sensing (2019) Vol. 11, Iss. 13, pp. 1589-1589
Open Access | Times Cited: 166
Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping
Phong Tung Nguyen, Duong Hai Ha, Mohammadtaghi Avand, et al.
Applied Sciences (2020) Vol. 10, Iss. 7, pp. 2469-2469
Open Access | Times Cited: 155
Robust machine learning algorithms for predicting coastal water quality index
Md Galal Uddin, Stephen Nash, Mir Talas Mahammad Diganta, et al.
Journal of Environmental Management (2022) Vol. 321, pp. 115923-115923
Open Access | Times Cited: 143
Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea
Wahyu Luqmanul Hakim, Fatemeh Rezaie, Arip Syaripudin Nur, et al.
Journal of Environmental Management (2021) Vol. 305, pp. 114367-114367
Closed Access | Times Cited: 142
Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization
Deliang Sun, Qingyu Gu, Haijia Wen, et al.
Gondwana Research (2022) Vol. 123, pp. 89-106
Closed Access | Times Cited: 94
Deep learning and benchmark machine learning based landslide susceptibility investigation, Garhwal Himalaya (India)
Soumik Saha, Paromita Majumdar, Biswajit Bera
Quaternary Science Advances (2023) Vol. 10, pp. 100075-100075
Open Access | Times Cited: 44
Landslide susceptibility zonation using the analytical hierarchy process (AHP) in the Great Xi’an Region, China
Xiaokang Liu, Shuai Shao, Shengjun Shao
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 22
Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam
Phong Tung Nguyen, Duong Hai Ha, Abolfazl Jaafari, et al.
International Journal of Environmental Research and Public Health (2020) Vol. 17, Iss. 7, pp. 2473-2473
Open Access | Times Cited: 125
Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods
Wei Chen, Paraskevas Tsangaratos, Ioanna Ilia, et al.
The Science of The Total Environment (2019) Vol. 684, pp. 31-49
Closed Access | Times Cited: 122
New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed
Dieu Tien Bui, Ataollah Shirzadi, Himan Shahabi, et al.
Forests (2019) Vol. 10, Iss. 9, pp. 743-743
Open Access | Times Cited: 118
A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping
Binh Thai Pham, Tran Van Phong, Huu Duy Nguyen, et al.
Water (2020) Vol. 12, Iss. 1, pp. 239-239
Open Access | Times Cited: 111
An Optimized Random Forest Model and Its Generalization Ability in Landslide Susceptibility Mapping: Application in Two Areas of Three Gorges Reservoir, China
Deliang Sun, Jiahui Xu, Haijia Wen, et al.
Journal of Earth Science (2020) Vol. 31, Iss. 6, pp. 1068-1086
Closed Access | Times Cited: 98
River Water Salinity Prediction Using Hybrid Machine Learning Models
Assefa M. Melesse, Khabat Khosravi, John P. Tiefenbacher, et al.
Water (2020) Vol. 12, Iss. 10, pp. 2951-2951
Open Access | Times Cited: 95
Landslide susceptibility modeling using different artificial intelligence methods: a case study at Muong Lay district, Vietnam
Tran Van Phong, Phan Trọng Trịnh, Indra Prakash, et al.
Geocarto International (2019) Vol. 36, Iss. 15, pp. 1685-1708
Closed Access | Times Cited: 92
Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete
Dong Van Dao, Haï-Bang Ly, Huong-Lan Thi Vu, et al.
Materials (2020) Vol. 13, Iss. 5, pp. 1072-1072
Open Access | Times Cited: 88
Landslide susceptibility assessment in a lesser Himalayan road corridor (India) applying fuzzy AHP technique and earth-observation data
Ujjwal Sur, Prafull Singh, Sansar Raj Meena
Geomatics Natural Hazards and Risk (2020) Vol. 11, Iss. 1, pp. 2176-2209
Open Access | Times Cited: 85
Application of logistic regression (LR) and frequency ratio (FR) models for landslide susceptibility mapping in Relli Khola river basin of Darjeeling Himalaya, India
Goutam Das, Kabita Lepcha
SN Applied Sciences (2019) Vol. 1, Iss. 11
Open Access | Times Cited: 81
Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria
Thimmaiah Gudiyangada Nachappa, Omid Ghorbanzadeh, Khalil Gholamnia, et al.
Remote Sensing (2020) Vol. 12, Iss. 17, pp. 2757-2757
Open Access | Times Cited: 80
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