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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 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: 135

Showing 26-50 of 135 citing articles:

Groundwater quality parameters prediction based on data-driven models
Mohammed Falah Allawi, Yasir Al-Ani, Arkan Dhari Jalal, et al.
Engineering Applications of Computational Fluid Mechanics (2024) Vol. 18, Iss. 1
Open Access | Times Cited: 7
Enhancing groundwater quality assessment in coastal area: A hybrid modeling approach
Md Galal Uddin, M. M. Shah Porun Rana, Mir Talas Mahammad Diganta, et al.
Heliyon (2024) Vol. 10, Iss. 13, pp. e33082-e33082
Open Access | Times Cited: 7
Sensitivity analysis-driven machine learning approach for groundwater quality prediction: Insights from integrating ENTROPY and CRITIC methods
Imran Khan, Md. Ayaz
Groundwater for Sustainable Development (2024) Vol. 26, pp. 101309-101309
Closed Access | Times Cited: 7
Toward Design of Internet of Things and Machine Learning-Enabled Frameworks for Analysis and Prediction of Water Quality
Mushtaque Ahmed Rahu, Abdul Fattah Chandio, Khursheed Aurangzeb, et al.
IEEE Access (2023) Vol. 11, pp. 101055-101086
Open Access | Times Cited: 19
A New Benchmark on Machine Learning Methodologies for Hydrological Processes Modelling: A Comprehensive Review for Limitations and Future Research Directions
Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬
Knowledge-Based Engineering and Sciences (2023) Vol. 4, Iss. 3, pp. 65-103
Open Access | Times Cited: 18
A critical analysis of parameter choices in water quality assessment
Hossein Moeinzadeh, Ken‐Tye Yong, Anusha Withana
Water Research (2024) Vol. 258, pp. 121777-121777
Open Access | Times Cited: 6
Assessing nitrate groundwater hotspots in Europe reveals an inadequate designation of Nitrate Vulnerable Zones
João Serra, C. Marques-dos-Santos, Joana Marinheiro, et al.
Chemosphere (2024) Vol. 355, pp. 141830-141830
Open Access | Times Cited: 5
Enhancing interpretability of tree-based models for downstream salinity prediction: Decomposing feature importance using the Shapley additive explanation approach
Guang-yao Zhao, Kenji Ohsu, Henry Kasmanhadi Saputra, et al.
Results in Engineering (2024) Vol. 23, pp. 102373-102373
Open Access | Times Cited: 5
Unlocking the Potential of Artificial Intelligence for Sustainable Water Management Focusing Operational Applications
J. Drisya, Adel Bouhoula, Waleed Al-Zubari
Water (2024) Vol. 16, Iss. 22, pp. 3328-3328
Open Access | Times Cited: 5
A novel spectroscopy-deep learning approach for aqueous multi-heavy metal detection
Zhizhi Fu, Qianru Wan, Qiannan Duan, et al.
Analytical Methods (2025)
Closed Access
A comparative hydrochemical assessment of groundwater quality for drinking and irrigation purposes using different statistical and ML models in lower gangetic alluvial plain, eastern India
Sribas Kanji, Subhasish Das, Chandi Rajak
Chemosphere (2025) Vol. 372, pp. 144074-144074
Closed Access
Machine Learning Prediction of Tritium‐Helium Groundwater Ages in the Central Valley, California, USA
Abdullah Azhar, C. Roca, Ate Visser, et al.
Water Resources Research (2025) Vol. 61, Iss. 1
Open Access
Data-Driven Insights into Climate Change Effects on Groundwater Levels Using Machine Learning
Xueqiang Lu, Zhewen Wang, Menghao Zhao, et al.
Water Resources Management (2025)
Closed Access
AI and IoT: Supported Sixth Generation Sensing for Water Quality Assessment to Empower Sustainable Ecosystems
Suparna Das, Kamil Reza Khondakar, Hirak Mazumdar, et al.
ACS ES&T Water (2025)
Closed Access
Digital technologies for water use and management in agriculture: Recent applications and future outlook
Carlos Parra-López, Saker Ben Abdallah, Guillermo García-García, et al.
Agricultural Water Management (2025) Vol. 309, pp. 109347-109347
Open Access
An Optimized Approach for Predicting Water Quality Features and A Performance evaluation for Mapping Surface Water Potential Zones Based on Discriminant Analysis (DA), Geographical Information System (GIS) and Machine Learning (ML) Models in Baitarani River Basin, Odisha
Abhijeet Das
Desalination and Water Treatment (2025) Vol. 321, pp. 101039-101039
Open Access
Comparison and prediction of shallow groundwater nitrate in Shaying River basin based on urban distribution using multiple machine learning approaches
Zipeng Huang, Baonan He, Yanjia Chu, et al.
Water Environment Research (2025) Vol. 97, Iss. 2
Closed Access
Comparative Assessment of Machine Learning Models for Groundwater Quality Prediction Using Various Parameters
Majid Niazkar, Reza Piraei, Mohammad Reza Goodarzi, et al.
Environmental Processes (2025) Vol. 12, Iss. 1
Closed Access
Assessing the Impact of Reverse Osmosis Plant Operations on Water Quality Index Improvement through Machine Learning Approaches and Health Risk Assessment
Fariba Abbasi, Azadeh Kazemi, Ahmad Badeenezhad, et al.
Results in Engineering (2025), pp. 104363-104363
Open Access
Machine Learning Techniques in Hydrogeological Research
Song He, Xiaoping Zhou, Yuan Liu, et al.
Springer hydrogeology (2025), pp. 137-164
Closed Access
Machine learning approaches to identify hydrochemical processes and predict drinking water quality for groundwater environment in a metropolis
Zhan Xie, Weiting Liu, Si Chen, et al.
Journal of Hydrology Regional Studies (2025) Vol. 58, pp. 102227-102227
Closed Access
Importance Analysis of Key Parameters for Coagulation Dosage Prediction Based on Machine Learning Models
Shijie Wang, Pinkun He, Yaning Yang, et al.
(2025)
Closed Access
Beyond One-Size-Fits-All: Differentiated Green Development Assessment Integrating a Hybrid Approach in China's Yangtze River Economic Belt
Linzi Li, Chenning Deng, Fang Zhu, et al.
(2025)
Closed Access
Using multiple machine learning algorithms to optimize the water quality index model and their applicability
Fei Ding, Shilong Hao, Wenjie Zhang, et al.
Ecological Indicators (2025) Vol. 172, pp. 113299-113299
Open Access
Machine Learning Analysis of Hydrological and Hydrochemical Data from the Abelar Pilot Basin in Abegondo (Coruña, Spain)
Javier Samper-Pilar, Javier Samper, Alba Mon, et al.
Hydrology (2025) Vol. 12, Iss. 3, pp. 49-49
Open Access

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