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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:

Groundwater Potential Mapping in Hubei Region of China Using Machine Learning, Ensemble Learning, Deep Learning and AutoML Methods
Zhigang Bai, Qimeng Liu, Yu Liu
Natural Resources Research (2022) Vol. 31, Iss. 5, pp. 2549-2569
Closed Access | Times Cited: 42

Showing 1-25 of 42 citing articles:

Application, interpretability and prediction of machine learning method combined with LSTM and LightGBM-a case study for runoff simulation in an arid area
Ling-Zhu Bian, Xueer Qin, Chenglong Zhang, et al.
Journal of Hydrology (2023) Vol. 625, pp. 130091-130091
Closed Access | Times Cited: 44
A secondary modal decomposition ensemble deep learning model for groundwater level prediction using multi-data
Xuefei Cui, Zhaocai Wang, Nannan Xu, et al.
Environmental Modelling & Software (2024) Vol. 175, pp. 105969-105969
Closed Access | Times Cited: 23
Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia
Fahad Alshehri, Atiqur Rahman
Water (2023) Vol. 15, Iss. 12, pp. 2298-2298
Open Access | Times Cited: 28
Automated parameter estimation for geothermal reservoir modeling using machine learning
Anna Suzuki, Shuokun Shi, Taro Sakai, et al.
Renewable Energy (2024) Vol. 224, pp. 120243-120243
Open Access | Times Cited: 8
Monitoring groundwater potential dynamics of north-eastern Bengal Basin in Bangladesh using AHP-Machine learning approaches
Biplob Dey, Kazi Al Muqtadir Abir, Romel Ahmed, et al.
Ecological Indicators (2023) Vol. 154, pp. 110886-110886
Open Access | Times Cited: 19
Mapping Groundwater Potential Zones in the Habawnah Basin of Southern Saudi Arabia: An AHP- and GIS-based Approach
Abdulnoor A. J. Ghanim, Ahmed M. Al‐Areeq, Mohammed Benaafi, et al.
Sustainability (2023) Vol. 15, Iss. 13, pp. 10075-10075
Open Access | Times Cited: 16
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
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
Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam
Huu Duy Nguyen, Van Hong Nguyen, Quan Vu Viet Du, et al.
Earth Science Informatics (2024) Vol. 17, Iss. 2, pp. 1569-1589
Closed Access | Times Cited: 6
Risk assessment of nitrate groundwater contamination using GIS-based machine learning methods: A case study in the northern Anhui plain, China
Kai Chen, Qimeng Liu, Tingting Yang, et al.
Journal of Contaminant Hydrology (2024) Vol. 261, pp. 104300-104300
Closed Access | Times Cited: 6
Spatial modeling of brine level and salinity in the Qarhan Salt Lake using GIS and automated machine learning algorithms
Dongmei Yu, Zitao Wang, Chao Yue, et al.
Journal of Hydrology Regional Studies (2025) Vol. 58, pp. 102195-102195
Closed Access
Performance evaluation of convolutional neural network and vision transformer models for groundwater potential mapping
Behnam Sadeghi, Ali Asghar Alesheikh, Ali Jafari, et al.
Journal of Hydrology (2025), pp. 132840-132840
Closed Access
Machine Learning Techniques in Hydrogeological Research
Song He, Xiaoping Zhou, Yuan Liu, et al.
Springer hydrogeology (2025), pp. 137-164
Closed Access
Groundwater Potential Mapping Using Remote Sensing and Random Forest Machine Learning Model: A Case Study from Lower Part of Wadi Yalamlam, Western Saudi Arabia
Ahmed Madani, Burhan Niyazi
Sustainability (2023) Vol. 15, Iss. 3, pp. 2772-2772
Open Access | Times Cited: 12
Groundwater prospectivity modeling over the Akatsi Districts in the Volta Region of Ghana using the frequency ratio technique
Prince Ofori Amponsah, Eric Dominic Forson, Prospera Sungpour Sungzie, et al.
Modeling Earth Systems and Environment (2022) Vol. 9, Iss. 1, pp. 937-955
Closed Access | Times Cited: 17
Groundwater potential delineation using geodetector based convolutional neural network in the Gunabay watershed of Ethiopia
Asnakew Mulualem Tegegne, Tarun Kumar Lohani, Abunu Atlabachew Eshete
Environmental Research (2023) Vol. 242, pp. 117790-117790
Closed Access | Times Cited: 9
Spatial Decision Support Systems with Automated Machine Learning: A Review
Richard Wen, Songnian Li
ISPRS International Journal of Geo-Information (2022) Vol. 12, Iss. 1, pp. 12-12
Open Access | Times Cited: 12
A Weighted Mean Temperature Forecast Model Based on Fused Data and Generalized Regression Neural Network and Its Impact on GNSS-based Precipitable Water Vapor Estimation
Junyu Li, Feijuan Li, Lilong Liu, et al.
IEEE Transactions on Geoscience and Remote Sensing (2024) Vol. 62, pp. 1-14
Closed Access | Times Cited: 2
Integrated machine learning and remote sensing for groundwater potential mapping in the Mekong Delta in Vietnam
Huu Duy Nguyen, Quoc‐Huy Nguyen, Dinh Kha Dang, et al.
Acta Geophysica (2024)
Closed Access | Times Cited: 2
Application of Machine Learning Algorithms in Predicting Extreme Rainfall Events in Rwanda
James Kagabo, Giri Kattel, Jonah Kazora, et al.
Atmosphere (2024) Vol. 15, Iss. 6, pp. 691-691
Open Access | Times Cited: 2
Iron Ore Price Forecast based on a Multi-Echelon Tandem Learning Model
Weixu Pan, Shi Qiang Liu, Mustafa Kumral, et al.
Natural Resources Research (2024) Vol. 33, Iss. 5, pp. 1969-1992
Closed Access | Times Cited: 2
Groundwater potential mapping in arid and semi-arid regions of Kurdistan region of Iraq: A geoinformatics-based machine learning approach
Kaiwan K. Fatah, Yaseen T. Mustafa, Imaddadin O. Hassan
Groundwater for Sustainable Development (2024), pp. 101337-101337
Closed Access | Times Cited: 2
A hybrid intelligent model for spatial analysis of groundwater potential around Urmia Lake, Iran
Omid Asadi Nalivan, Seyed Ali Mousavi Tayebi, Mohammad Mehrabi, et al.
Stochastic Environmental Research and Risk Assessment (2022) Vol. 37, Iss. 5, pp. 1821-1838
Closed Access | Times Cited: 12
Investigating machine learning and ensemble learning models in groundwater potential mapping in arid region: case study from Tan-Tan water-scarce region, Morocco
Abdessamad Jari, El Mostafa Bachaoui, Soufiane Hajaj, et al.
Frontiers in Water (2023) Vol. 5
Open Access | Times Cited: 5
Performance of Naïve Bayes Tree with ensemble learner techniques for groundwater potential mapping
Tran Van Phong, Binh Thai Pham
Physics and Chemistry of the Earth Parts A/B/C (2023) Vol. 132, pp. 103503-103503
Closed Access | Times Cited: 4
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