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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:

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

Showing 1-25 of 125 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
Groundwater level prediction using machine learning models: A comprehensive review
Tao Hai, Mohammed Majeed Hameed, Haydar Abdulameer Marhoon, et al.
Neurocomputing (2022) Vol. 489, pp. 271-308
Open Access | Times Cited: 263
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
GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models
Wei Chen, Yang Li
CATENA (2020) Vol. 195, pp. 104777-104777
Closed Access | Times Cited: 204
Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India
Kanak N. Moharir, Chaitanya B. Pande, Vinay Kumar Gautam, et al.
Environmental Research (2023) Vol. 228, pp. 115832-115832
Closed Access | Times Cited: 125
Critical role of climate factors for groundwater potential mapping in arid regions: Insights from random forest, XGBoost, and LightGBM algorithms
Xu Guo, Xiaofan Gui, Hanxiang Xiong, et al.
Journal of Hydrology (2023) Vol. 621, pp. 129599-129599
Closed Access | Times Cited: 80
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
Artificial Intelligence in Environmental Monitoring: Advancements, Challenges, and Future Directions
David B. Olawade, Ojima Z. Wada, Abimbola O. Ige, et al.
Hygiene and Environmental Health Advances (2024), pp. 100114-100114
Open Access | Times Cited: 27
Groundwater potential zone mapping using GIS and Remote Sensing based models for sustainable groundwater management
Abdur Rehman, Fakhrul Islam, Aqil Tariq, et al.
Geocarto International (2024) Vol. 39, Iss. 1
Open Access | Times Cited: 22
Mapping groundwater potential zone by robust machine learning algorithm & remote sing techniques in agriculture dominated area, Bangladesh.
M. M. Shah Porun Rana, Muhammad Tauhidur Rahman, Md. Fuad Hassan
Cleaner Water (2025), pp. 100064-100064
Open Access | Times Cited: 2
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
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
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
Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential
Yunzhi Chen, Wei Chen, Subodh Chandra Pal, et al.
Geocarto International (2021) Vol. 37, Iss. 19, pp. 5564-5584
Closed Access | Times Cited: 97
Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques
Alireza Arabameri, Subodh Chandra Pal, Fatemeh Rezaie, et al.
Journal of Hydrology Regional Studies (2021) Vol. 36, pp. 100848-100848
Open Access | Times Cited: 97
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
Groundwater potential mapping using multi-criteria decision, bivariate statistic and machine learning algorithms: evidence from Chota Nagpur Plateau, India
Md Hasanuzzaman, Mehedi Hasan Mandal, Md Hasnine, et al.
Applied Water Science (2022) Vol. 12, Iss. 4
Open Access | Times Cited: 59
Preprocessing approaches in machine-learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali
Víctor Gómez‐Escalonilla, Pedro Martínez‐Santos, Miguel Martín-Loeches
Hydrology and earth system sciences (2022) Vol. 26, Iss. 2, pp. 221-243
Open Access | Times Cited: 49
Groundwater Potential Mapping in Hubei Region of China Using Machine Learning, Ensemble Learning, Deep Learning and AutoML Methods
Zhigang Bai, Qimeng Liu, Yu Liu
Natural Resources Research (2022) Vol. 31, Iss. 5, pp. 2549-2569
Closed Access | Times Cited: 43
Machine learning and GIS-RS-based algorithms for mapping the groundwater potentiality in the Bundelkhand region, India
Mukesh Kumar, Pitam Singh, Priyamvada Singh
Ecological Informatics (2023) Vol. 74, pp. 101980-101980
Closed Access | Times Cited: 41
Novel Ensemble Machine Learning Modeling Approach for Groundwater Potential Mapping in Parbhani District of Maharashtra, India
Md Masroor, Haroon Sajjad, Pankaj Kumar, et al.
Water (2023) Vol. 15, Iss. 3, pp. 419-419
Open Access | Times Cited: 29
Using Artificial Intelligence to Identify Suitable Artificial Groundwater Recharge Areas for the Iranshahr Basin
Mojtaba Zaresefat, Reza Derakhshani, Vahid Nikpeyman, et al.
Water (2023) Vol. 15, Iss. 6, pp. 1182-1182
Open Access | Times Cited: 23
Sustainable groundwater management in coastal cities: Insights from groundwater potential and vulnerability using ensemble learning and knowledge-driven models
P. M. Huang, Mengyao Hou, Tong Sun, et al.
Journal of Cleaner Production (2024) Vol. 442, pp. 141152-141152
Closed Access | Times Cited: 12
Assessment of groundwater potential and determination of influencing factors using remote sensing and machine learning algorithms: A study of Nainital district of Uttarakhand state, India
Yatendra Sharma, Raihan Ahmed, Tamal Kanti Saha, et al.
Groundwater for Sustainable Development (2024) Vol. 25, pp. 101094-101094
Closed Access | Times Cited: 11
Assessment of Groundwater Potential Zones by Integrating Hydrogeological Data, Geographic Information Systems, Remote Sensing, and Analytical Hierarchical Process Techniques in the Jinan Karst Spring Basin of China
Portia Annabelle Opoku, Longcang Shu, George Kwame Amoako-Nimako
Water (2024) Vol. 16, Iss. 4, pp. 566-566
Open Access | Times Cited: 11
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