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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:

GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models
Viet‐Ha Nhu, Saeid Janizadeh, Mohammadtaghi Avand, et al.
Applied Sciences (2020) Vol. 10, Iss. 6, pp. 2039-2039
Open Access | Times Cited: 98

Showing 1-25 of 98 citing articles:

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
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
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
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
GIS-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India
Nur Islam Saikh, Prolay Mondal
Natural Hazards Research (2023) Vol. 3, Iss. 3, pp. 420-436
Open Access | Times Cited: 46
Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates
Siham Acharki, Ali Raza, Dinesh Kumar Vishwakarma, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 5
Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping
Binh Thai Pham, T. Nguyen‐Thoi, Chongchong Qi, et al.
CATENA (2020) Vol. 195, pp. 104805-104805
Closed Access | Times Cited: 136
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
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
Deep learning neural networks for spatially explicit prediction of flash flood probability
Mahdi Panahi, Abolfazl Jaafari, Ataollah Shirzadi, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101076-101076
Open Access | Times Cited: 120
Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques
Binh Thai Pham, Abolfazl Jaafari, Tran Van Phong, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101105-101105
Open Access | Times Cited: 95
GIS-based spatial modeling of snow avalanches using four novel ensemble models
Peyman Yariyan, Mohammadtaghi Avand, Rahim Ali Abbaspour, et al.
The Science of The Total Environment (2020) Vol. 745, pp. 141008-141008
Closed Access | Times Cited: 78
Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms
Viet‐Ha Nhu, Ayub Mohammadi, Himan Shahabi, et al.
Forests (2020) Vol. 11, Iss. 8, pp. 830-830
Open Access | Times Cited: 78
Ensemble of Machine-Learning Methods for Predicting Gully Erosion Susceptibility
Subodh Chandra Pal, Alireza Arabameri, Thomas Blaschke, et al.
Remote Sensing (2020) Vol. 12, Iss. 22, pp. 3675-3675
Open Access | Times Cited: 76
Implementation of Artificial Intelligence Based Ensemble Models for Gully Erosion Susceptibility Assessment
Indrajit Chowdhuri, Subodh Chandra Pal, Alireza Arabameri, et al.
Remote Sensing (2020) Vol. 12, Iss. 21, pp. 3620-3620
Open Access | Times Cited: 72
Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran
Viet‐Ha Nhu, Danesh Zandi, Himan Shahabi, et al.
Applied Sciences (2020) Vol. 10, Iss. 15, pp. 5047-5047
Open Access | Times Cited: 71
Artificial Intelligence in Agricultural Mapping: A Review
Ramón Espinel, Gricelda Herrera-Franco, José Luis Rivadeneira García, et al.
Agriculture (2024) Vol. 14, Iss. 7, pp. 1071-1071
Open Access | Times Cited: 13
Implementation of random forest, adaptive boosting, and gradient boosting decision trees algorithms for gully erosion susceptibility mapping using remote sensing and GIS
Hassan Ait Naceur, Hazem Ghassan Abdo, Brahim Igmoullan, et al.
Environmental Earth Sciences (2024) Vol. 83, Iss. 3
Closed Access | Times Cited: 9
New neural fuzzy-based machine learning ensemble for enhancing the prediction accuracy of flood susceptibility mapping
Romulus Costache, Roxana Ţîncu, Ismail Elkhrachy, et al.
Hydrological Sciences Journal (2020) Vol. 65, Iss. 16, pp. 2816-2837
Closed Access | Times Cited: 64
Naïve Bayes ensemble models for groundwater potential mapping
Binh Thai Pham, Abolfazl Jaafari, Tran Van Phong, et al.
Ecological Informatics (2021) Vol. 64, pp. 101389-101389
Closed Access | Times Cited: 53
Determining prone areas to gully erosion and the impact of land use change on it by using multiple-criteria decision-making algorithm in arid and semi-arid regions
Marzieh Mokarram, Abdol Rassoul Zarei
Geoderma (2021) Vol. 403, pp. 115379-115379
Closed Access | Times Cited: 52
Applying different resampling strategies in machine learning models to predict head-cut gully erosion susceptibility
Fengjie Wang, Mehebub Sahana, Bahareh Pahlevanzadeh, et al.
Alexandria Engineering Journal (2021) Vol. 60, Iss. 6, pp. 5813-5829
Open Access | Times Cited: 50
Deep learning and boosting framework for piping erosion susceptibility modeling: spatial evaluation of agricultural areas in the semi-arid region
Yunzhi Chen, Wei Chen, Saeid Janizadeh, et al.
Geocarto International (2021) Vol. 37, Iss. 16, pp. 4628-4654
Closed Access | Times Cited: 48
Understanding land degradation induced by gully erosion from the perspective of different geoenvironmental factors
Abolfazl Jaafari, Saeid Janizadeh, Hazem Ghassan Abdo, et al.
Journal of Environmental Management (2022) Vol. 315, pp. 115181-115181
Closed Access | Times Cited: 35
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