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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:

Assessment of groundwater arsenic contamination using machine learning in Varanasi, Uttar Pradesh, India
Siddharth Kumar, Jayadeep Pati
Journal of Water and Health (2022) Vol. 20, Iss. 5, pp. 829-848
Open Access | Times Cited: 18

Showing 18 citing articles:

Application of machine learning in groundwater quality modeling - A comprehensive review
Ryan Haggerty, Jianxin Sun, Hongfeng Yu, et al.
Water Research (2023) Vol. 233, pp. 119745-119745
Open Access | Times Cited: 148
An integrated appraisal of the hydrogeochemistry and the potential public health risks of groundwater nitrate and fluoride in eastern Ghana
Johnbosco C. Egbueri, Mahamuda Abu, Johnson C. Agbasi
Groundwater for Sustainable Development (2024) Vol. 26, pp. 101264-101264
Closed Access | Times Cited: 22
Arsenic contamination in GBM plains with a focus on Bihar, India: risks and remediation
Nishita Ivy
Discover Environment (2025) Vol. 3, Iss. 1
Open Access | Times Cited: 2
Spatiotemporal drivers of urban water pollution: Assessment of 102 cities across the Yangtze River Basin
Yilin Zhao, Han-Jun Sun, Xiaodan Wang, et al.
Environmental Science and Ecotechnology (2024) Vol. 20, pp. 100412-100412
Open Access | Times Cited: 8
Leveraging machine learning in porous media
Mostafa Delpisheh, Benyamin Ebrahimpour, Abolfazl Fattahi, et al.
Journal of Materials Chemistry A (2024) Vol. 12, Iss. 32, pp. 20717-20782
Open Access | Times Cited: 8
Exploring temperature dynamics in Madhya Pradesh: a spatial-temporal analysis
Amit Kumar, Siddharth Kumar, Kuldeep Singh Rautela, et al.
Environmental Monitoring and Assessment (2023) Vol. 195, Iss. 11
Closed Access | Times Cited: 14
Prediction of Urban Surface Water Quality Scenarios Using Water Quality Index (WQI), Multivariate Techniques, and Machine Learning (ML) Models in Water Resources, in Baitarani River Basin, Odisha: Potential Benefits and Associated Challenges
Abhijeet Das
Earth Systems and Environment (2025)
Closed Access
Identifying the Local Influencing Factors of Arsenic Concentration in Suburban Soil: A Multiscale Geographically Weighted Regression Approach
Yuanli Zhu, Bo Liu, Gui Jin, et al.
Toxics (2024) Vol. 12, Iss. 3, pp. 229-229
Open Access | Times Cited: 2
Machine learning approach for assessment of arsenic levels using physicochemical properties of water, soil, elevation, and land cover
Siddharth Kumar, Jayadeep Pati
Environmental Monitoring and Assessment (2023) Vol. 195, Iss. 6
Closed Access | Times Cited: 6
Coastal Water Quality Modelling Using E. coli, Meteorological Parameters and Machine Learning Algorithms
Athanasios Tselemponis, Christos Stefanis, Elpida Giorgi, et al.
International Journal of Environmental Research and Public Health (2023) Vol. 20, Iss. 13, pp. 6216-6216
Open Access | Times Cited: 4
Artificial Intelligence Modelling to Support the Groundwater Chemistry-Dependent Selection of Groundwater Arsenic Remediation Approaches in Bangladesh
Ruohan Wu, Laura A. Richards, Ajmal Roshan, et al.
Water (2023) Vol. 15, Iss. 20, pp. 3539-3539
Open Access | Times Cited: 3
Objective Cost-Informed Cutoff Criteria Improve the Utility of Machine Learning Models of Environmental Hazards: A Case Study of Groundwater Arsenic Distribution in India
Ruohan Wu, David A. Polya
Exposure and Health (2023) Vol. 16, Iss. 3, pp. 619-631
Open Access | Times Cited: 3
Characteristics and Source Analysis of High-Arsenic Groundwater in Typical Watershed Areas of Tibet, China
Zhongwei Zhang, Liangjing Zhou, Yunjiao He, et al.
Water (2023) Vol. 15, Iss. 3, pp. 533-533
Open Access | Times Cited: 2
The Concentration of the Potentially Toxic Metals in Human Hair, Nails, Urine, Blood, and Air, and Their Impact on Human Health: A Review
O. P. Bansal
European Journal of Theoretical and Applied Sciences (2023) Vol. 1, Iss. 2, pp. 185-216
Open Access | Times Cited: 2
Key factors influencing arsenic phytotoxicity thresholds in south China acidic soils
Fenghua Ding, Guo Wang, Shuxin Liu, et al.
Heliyon (2023) Vol. 9, Iss. 9, pp. e19905-e19905
Open Access | Times Cited: 2
Research into the Optimal Regulation of the Groundwater Table and Quality in the Southern Plain of Beijing Using Geographic Information Systems Data and Machine Learning Algorithms
Chen Li, Baohui Men, Shiyang Yin, et al.
ISPRS International Journal of Geo-Information (2022) Vol. 11, Iss. 10, pp. 501-501
Open Access | Times Cited: 3
Prediction of arsenic concentration in groundwater of Chapainawabganj, Bangladesh: machine learning-based approach to spatial modeling
Mst Fatima Khatun, A.H.M. Selim Reza, Golam Sabbir Sattar, et al.
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 33, pp. 46023-46037
Closed Access
The machine learning and geostatistical approach for assessment of arsenic contamination levels using physicochemical properties of water
Arghya Chattopadhyay, Anand Singh, Siddharth Kumar, et al.
Water Science & Technology (2023) Vol. 88, Iss. 3, pp. 595-614
Open Access | Times Cited: 1
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