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 level monitoring network design with machine learning methods
Sadaf Teimoori, Mohammad Hessam Olya, Carol J. Miller
Journal of Hydrology (2023) Vol. 625, pp. 130145-130145
Open Access | Times Cited: 13

Showing 13 citing articles:

Applications of machine learning to water resources management: A review of present status and future opportunities
Ashraf Ahmed, Sakina Sayed, Antoifi Abdoulhalik, et al.
Journal of Cleaner Production (2024) Vol. 441, pp. 140715-140715
Open Access | Times Cited: 55

Remote sensing, GIS, and analytic hierarchy process-based delineation and sustainable management of potential groundwater zones: a case study of Jhargram district, West Bengal, India
Rajkumar Guria, Manoranjan Mishra, Surajit Dutta, et al.
Environmental Monitoring and Assessment (2023) Vol. 196, Iss. 1
Closed Access | Times Cited: 11

Multi-decadal groundwater observations reveal surprisingly stable levels in southwestern Europe
Rafael Chávez García Silva, Robert Reinecke, Nadım K. Copty, et al.
Communications Earth & Environment (2024) Vol. 5, Iss. 1
Open Access | Times Cited: 4

A Review of Current trends, Challenges, and Future Perspectives in Machine Learning Applications to Water Resources in Nepal
Shishir Chaulagain, Manoj Lamichhane, Urusha Chaulagain
Journal of Hazardous Materials Advances (2025), pp. 100678-100678
Open Access

Balancing Results from AI-Based Geostatistics versus Fuzzy Inference by Game Theory Bargaining to Improve a Groundwater Monitoring Network
Masoumeh Hashemi, Richard C. Peralta, Matt A. Yost
Machine Learning and Knowledge Extraction (2024) Vol. 6, Iss. 3, pp. 1871-1893
Open Access | Times Cited: 2

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

Machine learning prediction of health risk and spatial dependence of geogenic contaminated groundwater from the Hetao Basin, China
Peng Xia, Yifu Zhao, Xianjun Xie, et al.
Journal of Geochemical Exploration (2024) Vol. 262, pp. 107497-107497
Closed Access | Times Cited: 1

A novel groundwater monitoring network design framework for long-term and economical data monitoring
S. K. Jena
Groundwater for Sustainable Development (2024) Vol. 26, pp. 101252-101252
Closed Access | Times Cited: 1

Exploring machine learning models to predict the unfrozen water content in copper-contaminated clays
Edyta Nartowska, Parveen Sihag
Cold Regions Science and Technology (2024) Vol. 227, pp. 104296-104296
Closed Access | Times Cited: 1

Study on the AHP model improvement for the allocation of subsidiary groundwater monitoring networks
Gyoo-Bum Kim, Yong Sung Won, Jihye Kim, et al.
Journal of the geological society of Korea (2024) Vol. 61, Iss. 1, pp. 101-110
Closed Access

Study on the AHP model improvement for the allocation of subsidiary groundwater monitoring networks
Gyoo-Bum Kim, Yong Sung Won, Jihye Kim, et al.
Journal of the geological society of Korea (2024) Vol. 61, Iss. 1, pp. 1001-1010
Closed Access

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