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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:

A top-down deep learning model for predicting spatiotemporal dynamics of groundwater recharge
Xin Huang, Lei Gao, Nan Zhang, et al.
Environmental Modelling & Software (2023) Vol. 167, pp. 105778-105778
Open Access | Times Cited: 25

Showing 25 citing articles:

Enhanced Air Quality Prediction through Spatio-temporal Feature Sxtraction and Fusion: A Self-tuning Hybrid Approach with GCN and GRU
Bao Liu, Zhi Qi, Lei Gao
Water Air & Soil Pollution (2024) Vol. 235, Iss. 8
Open Access | Times Cited: 6
Groundwater dynamics clustering and prediction based on grey relational analysis and LSTM model: A case study in Beijing Plain, China
Yan Zhou, Qiulan Zhang, Guoying Bai, et al.
Journal of Hydrology Regional Studies (2024) Vol. 56, pp. 102011-102011
Open Access | Times Cited: 6
Enhancing Groundwater Recharge Prediction: A Feature Selection‐Based Deep Forest Model With Bayesian Optimisation
Bao Liu, Yaohua Sun, Lei Gao
Hydrological Processes (2024) Vol. 38, Iss. 10
Open Access | Times Cited: 5
Revolutionizing the Future of Hydrological Science: Impact of Machine Learning and Deep Learning amidst Emerging Explainable AI and Transfer Learning
Rajib Maity, Aman Srivastava, Subharthi Sarkar, et al.
Applied Computing and Geosciences (2024) Vol. 24, pp. 100206-100206
Open Access | Times Cited: 5
Optimizing urban water sustainability: Integrating deep learning, genetic algorithm, and CMIP6 GCM for groundwater potential zone prediction within a social-ecological-technological framework
Mahfuzur Rahman, Md. Monirul Islam, Hyeong-Joo Kim, et al.
Advances in Space Research (2024) Vol. 73, Iss. 12, pp. 5925-5948
Closed Access | Times Cited: 4
Fine-tuning long short-term memory models for seamless transition in hydrological modelling: From pre-training to post-application
Xueying Chen, Yuhang Zhang, Aizhong Ye, et al.
Environmental Modelling & Software (2025), pp. 106350-106350
Open Access
Bayesian uncertainty estimation of adsorption closure models in the computational simulation of contaminant transport
Rodolfo S. M. Freitas, Jacques Honigbaum, Souleymane Zio, et al.
Journal of Environmental Management (2025) Vol. 379, pp. 124708-124708
Open Access
Investigation into groundwater level prediction within a deep learning framework: Incorporating the spatial dynamics of adjacent wells
Zhenyue Han, Fawen Li, Yong Zhao, et al.
Journal of Hydrology (2025), pp. 133097-133097
Closed Access
Estimation of groundwater recharge rates in small watersheds using regression tree model
Gyoo-Bum Kim, Chan-Ik Hwang, Jihye Kim
Geosciences Journal (2025)
Closed Access
PPTformer: A novel hybrid model for enhanced long-term time series forecasting with extreme value focus
Jian Liu, Junkang Guo, Lei Gao, et al.
Knowledge-Based Systems (2025), pp. 113456-113456
Closed Access
A high-resolution map of diffuse groundwater recharge rates for Australia
Stephen Lee, Dylan J. Irvine, Clément Duvert, et al.
Hydrology and earth system sciences (2024) Vol. 28, Iss. 7, pp. 1771-1790
Open Access | Times Cited: 3
Enhancing Hydrological Variable Prediction through Multitask LSTM Models
Yuguang Yan, Gan Li, Qingliang Li, et al.
Water (2024) Vol. 16, Iss. 15, pp. 2156-2156
Open Access | Times Cited: 2
Combining residual convolutional LSTM with attention mechanisms for spatiotemporal forest cover prediction
Bao Liu, Siqi Chen, Lei Gao
Environmental Modelling & Software (2024) Vol. 183, pp. 106260-106260
Open Access | Times Cited: 2
Research on Water Quality Prediction of Mine Surroundings Based on Improved Machine Learning Algorithm
Xihui Liu, Jingwei Gao
Research Square (Research Square) (2024)
Open Access
Reply on RC3
Stephen Lee
(2024)
Open Access
Reply on RC1
Stephen Lee
(2024)
Open Access
Reply on RC2
Stephen Lee
(2024)
Open Access
Uncertainties in physical and tracer methods in actual groundwater recharge estimation in the thick loess deposits of China
Wanzhou Wang, Yun Xia, Jineng Sun, et al.
Journal of Hydrology (2024) Vol. 634, pp. 131127-131127
Closed Access
Research on Water Quality Prediction of Mine Surroundings Based on Improved Machine Learning Algorithm
Xihui Liu, Zhenqi Hu, Jingwei Gao
(2024)
Open Access
An improved container-based deep forest model for predicting groundwater recharge
Bao Liu, Yaohua Sun, Lei Gao
Journal of Physics Conference Series (2024) Vol. 2816, Iss. 1, pp. 012033-012033
Open Access
Conventional and futuristic approaches for the computation of groundwater recharge: A comprehensive review
Shamla Rasheed, Marykutty Abraham
Journal of Groundwater Science and Engineering (2024) Vol. 12, Iss. 4, pp. 428-452
Open Access
Integrated Deep Learning and Genetic Algorithm Approach for Groundwater Potential Zone Prediction Incorporating Cmip6 Gcm: Unveiling Synergies for Enhanced Water Resource Management
Mahfuzur Rahman, Md. Monirul Islam, Hyeong-Joo Kim, et al.
(2023)
Closed Access
A high-resolution map of diffuse groundwater recharge rates for Australia
Stephen Lee, Dylan J. Irvine, Clément Duvert, et al.
(2023)
Open Access
Comment on egusphere-2023-2414

(2023)
Open Access
Comment on egusphere-2023-2414
Brian Barnett
(2023)
Open Access
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