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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:

Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms
Alireza Arabameri, Biswajeet Pradhan, Hamid Reza Pourghasemi, et al.
Applied Sciences (2018) Vol. 8, Iss. 8, pp. 1369-1369
Open Access | Times Cited: 127

Showing 1-25 of 127 citing articles:

A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran
Alireza Arabameri, Khalil Rezaei, Artemi Cerdà, et al.
The Science of The Total Environment (2019) Vol. 660, pp. 443-458
Closed Access | Times Cited: 259
Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms
Mahdis Amiri, Hamid Reza Pourghasemi, Gholamabbas Ghanbarian, et al.
Geoderma (2019) Vol. 340, pp. 55-69
Closed Access | Times Cited: 222
Measuring, modelling and managing gully erosion at large scales: A state of the art
Matthias Vanmaercke, Panos Panagos, Tom Vanwalleghem, et al.
Earth-Science Reviews (2021) Vol. 218, pp. 103637-103637
Open Access | Times Cited: 201
GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches
Alireza Arabameri, Khalil Rezaei, Artemi Cerdà, et al.
The Science of The Total Environment (2018) Vol. 658, pp. 160-177
Open Access | Times Cited: 195
Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models
Ali Azareh, Omid Rahmati, Elham Rafiei-Sardooi, et al.
The Science of The Total Environment (2018) Vol. 655, pp. 684-696
Open Access | Times Cited: 190
Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion
Younes Garosi, Mohsen Sheklabadi, Christian Conoscenti, et al.
The Science of The Total Environment (2019) Vol. 664, pp. 1117-1132
Closed Access | Times Cited: 189
Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran
Alireza Arabameri, Sunil Saha, Jagabandhu Roy, et al.
Remote Sensing (2020) Vol. 12, Iss. 3, pp. 475-475
Open Access | Times Cited: 182
GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms
Alireza Arabameri, Biswajeet Pradhan, Khalil Rezaei, et al.
Journal of Mountain Science (2019) Vol. 16, Iss. 3, pp. 595-618
Closed Access | Times Cited: 149
Comparison of machine learning models for gully erosion susceptibility mapping
Alireza Arabameri, Wei Chen, Marco Loche, et al.
Geoscience Frontiers (2019) Vol. 11, Iss. 5, pp. 1609-1620
Open Access | Times Cited: 149
Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility
Shahab S. Band, Saeid Janizadeh, Subodh Chandra Pal, et al.
Sensors (2020) Vol. 20, Iss. 19, pp. 5609-5609
Open Access | Times Cited: 147
Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS
Alireza Arabameri, Biswajeet Pradhan, Khalil Rezaei
Journal of Environmental Management (2018) Vol. 232, pp. 928-942
Open Access | Times Cited: 162
Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs
Alireza Arabameri, Biswajeet Pradhan, Khalil Rezaei, et al.
Remote Sensing (2019) Vol. 11, Iss. 9, pp. 999-999
Open Access | Times Cited: 134
Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)
Hamid Reza Pourghasemi, Soheila Pouyan, Bahram Heidari, et al.
International Journal of Infectious Diseases (2020) Vol. 98, pp. 90-108
Open Access | Times Cited: 121
Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process
Hamid Reza Pourghasemi, Nitheshnirmal Sãdhasivam, Narges Kariminejad, et al.
Geoscience Frontiers (2020) Vol. 11, Iss. 6, pp. 2207-2219
Open Access | Times Cited: 115
Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility
Alireza Arabameri, M Yamani, Biswajeet Pradhan, et al.
The Science of The Total Environment (2019) Vol. 688, pp. 903-916
Closed Access | Times Cited: 114
Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques
Alireza Arabameri, Biswajeet Pradhan, Khalil Rezaei, et al.
CATENA (2019) Vol. 180, pp. 282-297
Closed Access | Times Cited: 100
Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India
Sunil Saha, Jagabandhu Roy, Alireza Arabameri, et al.
Sensors (2020) Vol. 20, Iss. 5, pp. 1313-1313
Open Access | Times Cited: 95
Comparative assessment using boosted regression trees, binary logistic regression, frequency ratio and numerical risk factor for gully erosion susceptibility modelling
Alireza Arabameri, Biswajeet Pradhan, Luigi Lombardo
CATENA (2019) Vol. 183, pp. 104223-104223
Open Access | Times Cited: 93
A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility
Alireza Arabameri, Sunil Saha, Jagabandhu Roy, et al.
The Science of The Total Environment (2020) Vol. 726, pp. 138595-138595
Closed Access | Times Cited: 91
Predicting heavy metal contents by applying machine learning approaches and environmental covariates in west of Iran
Kamran Azizi, Shamsollah Ayoubi, Kamal Nabiollahi, et al.
Journal of Geochemical Exploration (2021) Vol. 233, pp. 106921-106921
Closed Access | Times Cited: 91
Novel Ensemble of MCDM-Artificial Intelligence Techniques for Groundwater-Potential Mapping in Arid and Semi-Arid Regions (Iran)
Alireza Arabameri, Saro Lee, John P. Tiefenbacher, et al.
Remote Sensing (2020) Vol. 12, Iss. 3, pp. 490-490
Open Access | Times Cited: 80
A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia
Hejar Shahabi, Ben Jarihani, Sepideh Tavakkoli Piralilou, et al.
Sensors (2019) Vol. 19, Iss. 22, pp. 4893-4893
Open Access | Times Cited: 79
Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility
Alireza Arabameri, Omid Asadi Nalivan, Subodh Chandra Pal, et al.
Remote Sensing (2020) Vol. 12, Iss. 17, pp. 2833-2833
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
Using machine learning algorithms to map the groundwater recharge potential zones
Hamid Reza Pourghasemi, Nitheshnirmal Sãdhasivam, Saleh Yousefi, et al.
Journal of Environmental Management (2020) Vol. 265, pp. 110525-110525
Closed Access | Times Cited: 75
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