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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:

Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential
Seyed Amir Naghibi, Mojtaba Dolatkordestani, Ashkan Rezaei, et al.
Environmental Monitoring and Assessment (2019) Vol. 191, Iss. 4
Closed Access | Times Cited: 78

Showing 1-25 of 78 citing articles:

Ensemble machine learning paradigms in hydrology: A review
Mohammad Zounemat‐Kermani, Okke Batelaan, Marzieh Fadaee, et al.
Journal of Hydrology (2021) Vol. 598, pp. 126266-126266
Open Access | Times Cited: 440
Ensemble Boosting and Bagging Based Machine Learning Models for Groundwater Potential Prediction
Amirhosein Mosavi, Farzaneh Sajedi Hosseini, Bahram Choubin, et al.
Water Resources Management (2020) Vol. 35, Iss. 1, pp. 23-37
Closed Access | Times Cited: 215
Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods
Sina Ardabili, Amir Mosavi, Annamária R. Várkonyi-Kóczy
Lecture notes in networks and systems (2020), pp. 215-227
Closed Access | Times Cited: 171
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
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
Novel ensemble machine learning models in flood susceptibility mapping
Pankaj Prasad, Victor J. Loveson, Bappa Das, et al.
Geocarto International (2021) Vol. 37, Iss. 16, pp. 4571-4593
Closed Access | Times Cited: 106
Lung Cancer Risk Prediction with Machine Learning Models
Ηλίας Δρίτσας, Μαρία Τρίγκα
Big Data and Cognitive Computing (2022) Vol. 6, Iss. 4, pp. 139-139
Open Access | Times Cited: 85
The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers
Davoud Davoudi Moghaddam, Omid Rahmati, Mahdi Panahi, et al.
CATENA (2019) Vol. 187, pp. 104421-104421
Open Access | Times Cited: 119
Application of machine learning techniques in groundwater potential mapping along the west coast of India
Pankaj Prasad, Victor J. Loveson, Mahender Kotha, et al.
GIScience & Remote Sensing (2020) Vol. 57, Iss. 6, pp. 735-752
Closed Access | Times Cited: 116
Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater
Alireza Motevalli, Seyed Amir Naghibi, Hossein Hashemi, et al.
Journal of Cleaner Production (2019) Vol. 228, pp. 1248-1263
Closed Access | Times Cited: 108
Groundwater potential zones for sustainable management plans in a river basin of India and Bangladesh
Swades Pal, Sonali Kundu, Susanta Mahato
Journal of Cleaner Production (2020) Vol. 257, pp. 120311-120311
Closed Access | Times Cited: 108
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
A tree-based intelligence ensemble approach for spatial prediction of potential groundwater
Mohammadtaghi Avand, Saeid Janizadeh, Dieu Tien Bui, et al.
International Journal of Digital Earth (2020) Vol. 13, Iss. 12, pp. 1408-1429
Closed Access | Times Cited: 94
Flood susceptibility assessment using extreme gradient boosting (EGB), Iran
Sajjad Mirzaei, Mehdi Vafakhah, Biswajeet Pradhan, et al.
Earth Science Informatics (2020) Vol. 14, Iss. 1, pp. 51-67
Closed Access | Times Cited: 82
Groundwater aquifer potential modeling using an ensemble multi-adoptive boosting logistic regression technique
Hossein Mojaddadi Rizeei, Biswajeet Pradhan, Maryam Adel Saharkhiz, et al.
Journal of Hydrology (2019) Vol. 579, pp. 124172-124172
Closed Access | Times Cited: 81
Application of Advanced Machine Learning Algorithms to Assess Groundwater Potential Using Remote Sensing-Derived Data
Ehsan Kamali Maskooni, Seyed Amir Naghibi, Hossein Hashemi, et al.
Remote Sensing (2020) Vol. 12, Iss. 17, pp. 2742-2742
Open Access | Times Cited: 78
Flood susceptibility mapping of Northeast coastal districts of Tamil Nadu India using Multi-source Geospatial data and Machine Learning techniques
Subbarayan Saravanan, Devanantham Abijith
Geocarto International (2022) Vol. 37, Iss. 27, pp. 15252-15281
Closed Access | Times Cited: 46
Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review
Mojtaba Zaresefat, Reza Derakhshani
Water (2023) Vol. 15, Iss. 9, pp. 1750-1750
Open Access | Times Cited: 33
Empowered machine learning algorithm to identify sustainable groundwater potential zone map in Jashore District, Bangladesh
Sujit Kumar Roy, Md. Mahmudul Hasan, Ismail Mondal, et al.
Groundwater for Sustainable Development (2024) Vol. 25, pp. 101168-101168
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
Evaluating landslide susceptibility: the impact of resolution and hybrid integration approaches
Xia Zhao, Wei Chen, Paraskevas Tsangaratos, et al.
Geomatics Natural Hazards and Risk (2024) Vol. 15, Iss. 1
Open Access | Times Cited: 9
Performance evaluation of convolutional neural network and vision transformer models for groundwater potential mapping
Behnam Sadeghi, Ali Asghar Alesheikh, Ali Jafari, et al.
Journal of Hydrology (2025), pp. 132840-132840
Closed Access | Times Cited: 1
Optimized Conditioning Factors Using Machine Learning Techniques for Groundwater Potential Mapping
Bahareh Kalantar, Husam A. H. Al-Najjar, Biswajeet Pradhan, et al.
Water (2019) Vol. 11, Iss. 9, pp. 1909-1909
Open Access | Times Cited: 72
Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models
Amirhosein Mosavi, Farzaneh Sajedi Hosseini, Bahram Choubin, et al.
IEEE Access (2020) Vol. 8, pp. 145564-145576
Open Access | Times Cited: 67
Identifying sources of dust aerosol using a new framework based on remote sensing and modelling
Omid Rahmati, Farnoush Mohammadi, Seid Saeid Ghiasi, et al.
The Science of The Total Environment (2020) Vol. 737, pp. 139508-139508
Closed Access | Times Cited: 54
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