Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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 1-25 of 135 citing articles:

Developing a novel tool for assessing the groundwater incorporating water quality index and machine learning approach
Abdul Majed Sajib, Mir Talas Mahammad Diganta, Azizur Rahman, et al.
Groundwater for Sustainable Development (2023) Vol. 23, pp. 101049-101049
Open Access | Times Cited: 48
Trace element pollution tracking in the complex multi-aquifer groundwater system of Al-Hassa oasis (Saudi Arabia) using spatial, chemometric and index-based techniques
Sani I. Abba, Mohamed A. Yassin, Syed Muzzamil Hussain Shah, et al.
Environmental Research (2024) Vol. 249, pp. 118320-118320
Closed Access | Times Cited: 34
Conceptualizing future groundwater models through a ternary framework of multisource data, human expertise, and machine intelligence
Chuanjun Zhan, Zhenxue Dai, Shangxian Yin, et al.
Water Research (2024) Vol. 257, pp. 121679-121679
Closed Access | Times Cited: 24
Groundwater level forecasting with machine learning models: A review
Kenneth Beng Wee Boo, Ahmed El‐Shafie, Faridah Othman, et al.
Water Research (2024) Vol. 252, pp. 121249-121249
Closed Access | Times Cited: 23
Assessment of human health risk from potentially toxic elements and predicting groundwater contamination using machine learning approaches
Md Galal Uddin, Md. Hasan Imran, Abdul Majed Sajib, et al.
Journal of Contaminant Hydrology (2024) Vol. 261, pp. 104307-104307
Open Access | Times Cited: 22
Nitrate concentrations tracking from multi-aquifer groundwater vulnerability zones: Insight from machine learning and spatial mapping
Sani I. Abba, Mohamed A. Yassin, Mahmud M. Jibril, et al.
Process Safety and Environmental Protection (2024) Vol. 184, pp. 1143-1157
Closed Access | Times Cited: 13
Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer
Yuqi Wang, Hongcheng Wang, Yunpeng Song, et al.
Water Research (2023) Vol. 246, pp. 120676-120676
Closed Access | Times Cited: 37
Predictive modeling and analysis of key drivers of groundwater nitrate pollution based on machine learning
Yuandong Deng, Xueyan Ye, Xinqiang Du
Journal of Hydrology (2023) Vol. 624, pp. 129934-129934
Closed Access | Times Cited: 27
Revolutionizing Groundwater Management with Hybrid AI Models: A Practical Review
Mojtaba Zaresefat, Reza Derakhshani
Water (2023) Vol. 15, Iss. 9, pp. 1750-1750
Open Access | Times Cited: 26
Groundwater vulnerability and risk mitigation: A comprehensive review of the techniques and applications
Harshita Jain
Groundwater for Sustainable Development (2023) Vol. 22, pp. 100968-100968
Closed Access | Times Cited: 22
Viability of two adaptive fuzzy systems based on fuzzy c means and subtractive clustering methods for modeling Cadmium in groundwater resources
Naghmeh Jafarzade, Özgür Kişi, Mahmood Yousefi, et al.
Heliyon (2023) Vol. 9, Iss. 8, pp. e18415-e18415
Open Access | Times Cited: 21
Groundwater vulnerability assessment in central Iran: Integration of GIS-based DRASTIC model and a machine learning approach
Zeynab Karimzadeh Motlagh, Reza Derakhshani, Mohammad Hossein Sayadi
Groundwater for Sustainable Development (2023) Vol. 23, pp. 101037-101037
Open Access | Times Cited: 21
A coupled model to improve river water quality prediction towards addressing non-stationarity and data limitation
Shengyue Chen, Jinliang Huang, Peng Wang, et al.
Water Research (2023) Vol. 248, pp. 120895-120895
Closed Access | Times Cited: 21
Removal of the textile dyes by a resin adsorbent polymeric: Insight into optimization, kinetics and isotherms adsorption phenomenally
Jaouad Bensalah
Inorganic Chemistry Communications (2023) Vol. 161, pp. 111975-111975
Closed Access | Times Cited: 21
Robust remote sensing retrieval of key eutrophication indicators in coastal waters based on explainable machine learning
Liudi Zhu, Tingwei Cui, A Runa, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2024) Vol. 211, pp. 262-280
Closed Access | Times Cited: 13
Prediction of arsenic and fluoride in groundwater of the North China Plain using enhanced stacking ensemble learning
Wengeng Cao, Zhuo Zhang, Yu Fu, et al.
Water Research (2024) Vol. 259, pp. 121848-121848
Closed Access | Times Cited: 13
Sustainable groundwater management in coastal cities: Insights from groundwater potential and vulnerability using ensemble learning and knowledge-driven models
P. M. Huang, Mengyao Hou, Tong Sun, et al.
Journal of Cleaner Production (2024) Vol. 442, pp. 141152-141152
Closed Access | Times Cited: 11
On the challenges of global entity-aware deep learning models for groundwater level prediction
Benedikt Heudorfer, Tanja Liesch, Stefan Broda
Hydrology and earth system sciences (2024) Vol. 28, Iss. 3, pp. 525-543
Open Access | Times Cited: 11
AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods
Muhammad Usman Tariq, Shuhaida Ismail
Osong Public Health and Research Perspectives (2024) Vol. 15, Iss. 2, pp. 115-136
Open Access | Times Cited: 11
Remote Sensing Technologies for Unlocking New Groundwater Insights: A Comprehensive Review
Abba Ibrahim, Aimrun Wayayok, Helmi Zulhaidi Mohd Shafri, et al.
Journal of Hydrology X (2024) Vol. 23, pp. 100175-100175
Open Access | Times Cited: 10
An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy
Hussam Eldin Elzain, Osman Abdalla, Hamdi Abdurhman Ahmed, et al.
Journal of Environmental Management (2024) Vol. 351, pp. 119896-119896
Closed Access | Times Cited: 8
A unified new-information-based accumulating generation operator based on feature decoupling for multi-characteristic time series forecasting
Song Ding, Zhijian Cai, Juntao Ye, et al.
Applied Soft Computing (2024) Vol. 154, pp. 111310-111310
Closed Access | Times Cited: 8
Prediction of sulfate concentrations in groundwater in areas with complex hydrogeological conditions based on machine learning
Yushan Tian, Quanli Liu, Yao Ji, et al.
The Science of The Total Environment (2024) Vol. 923, pp. 171312-171312
Closed Access | Times Cited: 8
Advancing groundwater quality predictions: Machine learning challenges and solutions
Juan Antonio Torres-Martínez, Jürgen Mahlknecht, Manish Kumar, et al.
The Science of The Total Environment (2024) Vol. 949, pp. 174973-174973
Closed Access | Times Cited: 8
Shallow groundwater quality and health risk assessment of fluoride and arsenic in Northwestern Jiangsu Province, China
Wang Shou, Jing Chen, Shuxuan Zhang, et al.
Applied Water Science (2024) Vol. 14, Iss. 6
Open Access | Times Cited: 7
Page 1 - Next Page

Scroll to top