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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:

Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin
Víctor Gómez‐Escalonilla, Marie-Louise Vogt, Elisa Destro, et al.
Geocarto International (2021) Vol. 37, Iss. 25, pp. 8924-8951
Open Access | Times Cited: 25

Showing 25 citing articles:

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: 48
Mapping groundwater potential zones in Kanchanaburi Province, Thailand by integrating of analytic hierarchy process, frequency ratio, and random forest
Nguyễn Ngọc Thành, Srilert Chotpantarat, Trung Hieu Nguyen, et al.
Ecological Indicators (2022) Vol. 145, pp. 109591-109591
Open Access | Times Cited: 31
Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile valley, Upper Egypt
Hesham Morgan, Ahmed Madani, Hussien M. Hussien, et al.
Geoscience Letters (2023) Vol. 10, Iss. 1
Open Access | Times Cited: 20
Modelling of groundwater potential zone in hard rock-dominated drought-prone region of eastern India using integrated geospatial approach
Tanmoy Biswas, Subodh Chandra Pal, Dipankar Ruidas, et al.
Environmental Earth Sciences (2023) Vol. 82, Iss. 3
Closed Access | Times Cited: 16
Application of bagging and boosting ensemble machine learning techniques for groundwater potential mapping in a drought-prone agriculture region of eastern India
Krishnagopal Halder, Amit Kumar Srivastava, Anitabha Ghosh, et al.
Environmental Sciences Europe (2024) Vol. 36, Iss. 1
Open Access | Times Cited: 7
La integración de datos geológicos, hidrogeológicos y geofísicos con el fin de identificar los recursos de aguas subterráneas en zonas de basamento granítico (Macizo de Guéra, Chad)
Haroun Nouradine, Cyril Schamper, Danièle Valdés, et al.
Hydrogeology Journal (2024) Vol. 32, Iss. 3, pp. 759-784
Open Access | Times Cited: 4
An ensemble model of knowledge- and data-driven geospatial methods for mapping groundwater potential in a data-scarce, semi-arid fractured rock region
Stephen Fildes, Ian Clark, David Bruce, et al.
Applied Water Science (2025) Vol. 15, Iss. 4
Open Access
Effectiveness of machine learning ensemble models in assessing groundwater potential in Lidder watershed, India
Rayees Ali, Haroon Sajjad, Tamal Kanti Saha, et al.
Acta Geophysica (2023) Vol. 72, Iss. 4, pp. 2843-2856
Closed Access | Times Cited: 10
MaxEnt machine learning model predicts high groundwater potential areas in a fractured volcanic aquifer system
Stefano Ballardin, Rossano Belladona, Tiago de Vargas, et al.
Journal of South American Earth Sciences (2024) Vol. 135, pp. 104794-104794
Closed Access | Times Cited: 3
Proxy modeling approach to evaluate groundwater recharge potentiality zones in the data scarce area of upper Blue Nile Basin, Ethiopia
Asnakew Mulualem Tegegne, Tarun Kumar Lohani, Abunu Atlabachew Eshete
Environmental Monitoring and Assessment (2023) Vol. 195, Iss. 6
Closed Access | Times Cited: 8
A machine learning approach to site groundwater contamination monitoring wells
Víctor Gómez‐Escalonilla, Esperanza Montero-González, Silvia Díaz-Alcaide, et al.
Applied Water Science (2024) Vol. 14, Iss. 12
Open Access | Times Cited: 2
An Integration of Geospatial Modelling and Machine Learning Techniques for Mapping Groundwater Potential Zones in Nelson Mandela Bay, South Africa
Irvin D. Shandu, Iqra Atif
Water (2023) Vol. 15, Iss. 19, pp. 3447-3447
Open Access | Times Cited: 6
Potentialities and development of groundwater resources applying machine learning models in the extended section of Manbhum-Singhbhum Plateau, India
Arijit Ghosh, Biswajit Bera
HydroResearch (2023) Vol. 7, pp. 1-14
Open Access | Times Cited: 5
Machine Intelligence in Africa: a survey
Hamidou Tembiné, Allahsera Auguste Tapo, Sidy Danioko, et al.
(2024)
Open Access | Times Cited: 1
Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers
Víctor Gómez‐Escalonilla, O. Diancoumba, Dasso Yolande Traoré, et al.
Journal of Hydrology Regional Studies (2022) Vol. 44, pp. 101245-101245
Open Access | Times Cited: 6
Application of advanced machine learning algorithms and geospatial techniques for groundwater potential zone mapping in Gambela Plain, Ethiopia
Tesema Kebede Seifu, Kidist Demessie Eshetu, Tekalegn Ayele Woldesenbet, et al.
Hydrology Research (2023) Vol. 54, Iss. 10, pp. 1246-1266
Open Access | Times Cited: 3
A Review of Geological and Climatic Variables in Groundwater Availability Prediction in Africa: Machine Learning Approaches
Haoulata Touré, Cyril D. Boateng, Solomon Gidigasu, et al.
EarthArXiv (California Digital Library) (2024)
Open Access
A Surrogate Approach to Model Groundwater Level in Time and Space Based on Tree Regressors
Pedro Martínez‐Santos, Víctor Gómez‐Escalonilla, Silvia Díaz-Alcaide, et al.
(2024)
Closed Access
Review of machine learning algorithms used in groundwater availability studies in Africa: analysis of geological and climate input variables
Haoulata Touré, Cyril D. Boateng, Solomon Gidigasu, et al.
Discover Water (2024) Vol. 4, Iss. 1
Open Access
Geospatial mapping of groundwater potential zones using multi-criteria decision-making AHP approach: A Study of Kadugli District, South Kurdufan, Sudan
M. A. Hamdan, Rakesh Singh, R.K. Pathak, et al.
Journal of African Earth Sciences (2024), pp. 105513-105513
Closed Access
Identification of non-conventional groundwater resources by means of machine learning in the Aconcagua basin, Chile
M. Aliaga-Alvarado, Víctor Gómez‐Escalonilla, Pedro Martínez‐Santos
Journal of Hydrology Regional Studies (2023) Vol. 49, pp. 101502-101502
Open Access | Times Cited: 1
Characterization of potential recharge zone in the Biltine basement area, Eastern Chad: use of the multi-criteria approach
Abdallah Mahamat Nour, Kaouyo Bekain, Abderamane Hamit
Comptes Rendus Géoscience (2023) Vol. 355, Iss. G2, pp. 299-310
Open Access
Reply on RC2
Victor Gómez-Escalonilla
(2021)
Open Access
Reply on RC1
Victor Gómez-Escalonilla
(2021)
Open Access
Comment on hess-2021-261
Víctor Gà mez-Escalonilla, Pedro Martínez-Santos, Miguel Martín-Loeches
(2021)
Open Access
Page 1

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