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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:

Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution
Ataollah Shirzadi, Karim Solaimani, Mahmood Habibnejad Roshan, et al.
CATENA (2019) Vol. 178, pp. 172-188
Closed Access | Times Cited: 135

Showing 1-25 of 135 citing articles:

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
Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest
Emrehan Kutluğ Şahin
SN Applied Sciences (2020) Vol. 2, Iss. 7
Open Access | Times Cited: 258
Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models
Zhilu Chang, Zhen Du, Fan Zhang, et al.
Remote Sensing (2020) Vol. 12, Iss. 3, pp. 502-502
Open Access | Times Cited: 238
Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model
Faming Huang, Zhongshan Cao, Shui‐Hua Jiang, et al.
Landslides (2020) Vol. 17, Iss. 12, pp. 2919-2930
Closed Access | Times Cited: 236
Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
Viet‐Ha Nhu, Ataollah Shirzadi, Himan Shahabi, et al.
International Journal of Environmental Research and Public Health (2020) Vol. 17, Iss. 8, pp. 2749-2749
Open Access | Times Cited: 221
Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region
Yaning Yi, Zhijie Zhang, Wanchang Zhang, et al.
CATENA (2020) Vol. 195, pp. 104851-104851
Closed Access | Times Cited: 196
Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms
Binh Thai Pham, Ataollah Shirzadi, Himan Shahabi, et al.
Sustainability (2019) Vol. 11, Iss. 16, pp. 4386-4386
Open Access | Times Cited: 168
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
Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis
Binh Thai Pham, Manh Duc Nguyen, Dong Van Dao, et al.
The Science of The Total Environment (2019) Vol. 679, pp. 172-184
Closed Access | Times Cited: 151
Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India
Kanu Mandal, Sunil Saha, Sujit Mandal
Geoscience Frontiers (2021) Vol. 12, Iss. 5, pp. 101203-101203
Open Access | Times Cited: 131
Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models
Faming Huang, Ye Zhou, Shui‐Hua Jiang, et al.
CATENA (2021) Vol. 202, pp. 105250-105250
Closed Access | Times Cited: 112
Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples
Can Yang, Leilei Liu, Faming Huang, et al.
Gondwana Research (2022) Vol. 123, pp. 198-216
Closed Access | Times Cited: 94
Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility
Pedro Lima, Stefan Steger, Thomas Glade, et al.
Journal of Mountain Science (2022) Vol. 19, Iss. 6, pp. 1670-1698
Open Access | Times Cited: 81
Modelling landslide susceptibility prediction: A review and construction of semi-supervised imbalanced theory
Faming Huang, Haowen Xiong, Shui‐Hua Jiang, et al.
Earth-Science Reviews (2024) Vol. 250, pp. 104700-104700
Closed Access | Times Cited: 61
Flash flood susceptibility mapping using a novel deep learning model based on deep belief network, back propagation and genetic algorithm
Himan Shahabi, Ataollah Shirzadi, Somayeh Ronoud, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101100-101100
Open Access | Times Cited: 133
Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment
Viet‐Ha Nhu, Ayub Mohammadi, Himan Shahabi, et al.
International Journal of Environmental Research and Public Health (2020) Vol. 17, Iss. 14, pp. 4933-4933
Open Access | Times Cited: 121
GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran
Xinxiang Lei, Wei Chen, Mohammadtaghi Avand, et al.
Remote Sensing (2020) Vol. 12, Iss. 15, pp. 2478-2478
Open Access | Times Cited: 121
Can deep learning algorithms outperform benchmark machine learning algorithms in flood susceptibility modeling?
Binh Thai Pham, Chinh Luu, Tran Van Phong, et al.
Journal of Hydrology (2020) Vol. 592, pp. 125615-125615
Closed Access | Times Cited: 121
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
Shallow Landslide Prediction Using a Novel Hybrid Functional Machine Learning Algorithm
Dieu Tien Bui, Himan Shahabi, Ebrahim Omidvar, et al.
Remote Sensing (2019) Vol. 11, Iss. 8, pp. 931-931
Open Access | Times Cited: 116
Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran
Viet‐Ha Nhu, Ataollah Shirzadi, Himan Shahabi, et al.
Forests (2020) Vol. 11, Iss. 4, pp. 421-421
Open Access | Times Cited: 115
A Novel Ensemble Artificial Intelligence Approach for Gully Erosion Mapping in a Semi-Arid Watershed (Iran)
Dieu Tien Bui, Ataollah Shirzadi, Himan Shahabi, et al.
Sensors (2019) Vol. 19, Iss. 11, pp. 2444-2444
Open Access | Times Cited: 110
Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method
Sunil Saha, Alireza Arabameri, Anik Saha, et al.
The Science of The Total Environment (2020) Vol. 764, pp. 142928-142928
Closed Access | Times Cited: 107
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: 103
Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network
Li Zhu, Liang-Hao Huang, Linyu Fan, et al.
Sensors (2020) Vol. 20, Iss. 6, pp. 1576-1576
Open Access | Times Cited: 99
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