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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:

Modeling the Influence of Groundwater Exploitation on Land Subsidence Susceptibility Using Machine Learning Algorithms
Mahtab Zamanirad, Amirpouya Sarraf, Hossein Sedghi, et al.
Natural Resources Research (2019) Vol. 29, Iss. 2, pp. 1127-1141
Closed Access | Times Cited: 26

Showing 1-25 of 26 citing articles:

Machine learning-based techniques for land subsidence simulation in an urban area
Jianxin Liu, Wenxiang Liu, Fabrice Blanchard Allechy, et al.
Journal of Environmental Management (2024) Vol. 352, pp. 120078-120078
Closed Access | Times Cited: 39
Unveiling the Global Extent of Land Subsidence: The Sinking Crisis
Tsimur Davydzenka, Pejman Tahmasebi, Nima Shokri
Geophysical Research Letters (2024) Vol. 51, Iss. 4
Open Access | Times Cited: 18
Application of learning vector quantization and different machine learning techniques to assessing forest fire influence factors and spatial modelling
Hamid Reza Pourghasemi, Amiya Gayen, Rosa Lasaponara, et al.
Environmental Research (2020) Vol. 184, pp. 109321-109321
Closed Access | Times Cited: 109
State of the Art and Recent Advancements in the Modelling of Land Subsidence Induced by Groundwater Withdrawal
Artur Guzy, Agnieszka Malinowska
Water (2020) Vol. 12, Iss. 7, pp. 2051-2051
Open Access | Times Cited: 94
Susceptibility Prediction of Groundwater Hardness Using Ensemble Machine Learning Models
Amirhosein Mosavi, Farzaneh Sajedi Hosseini, Bahram Choubin, et al.
Water (2020) Vol. 12, Iss. 10, pp. 2770-2770
Open Access | Times Cited: 83
Land subsidence simulation considering groundwater and compressible layers based on an improved machine learning method
Liyuan Shi, Huili Gong, Beibei Chen, et al.
Journal of Hydrology (2025) Vol. 656, pp. 133008-133008
Closed Access | Times Cited: 1
Land Subsidence Prediction Induced by Multiple Factors Using Machine Learning Method
Liyuan Shi, Huili Gong, Beibei Chen, et al.
Remote Sensing (2020) Vol. 12, Iss. 24, pp. 4044-4044
Open Access | Times Cited: 43
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
Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining
Artur Guzy, Wojciech T. Witkowski
Energies (2021) Vol. 14, Iss. 15, pp. 4658-4658
Open Access | Times Cited: 25
Vegetation vulnerability to hydrometeorological stresses in water-scarce areas using machine learning and remote sensing techniques
Ehsan Moradi, Hamid Darabi, Esmail Heydari Alamdarloo, et al.
Ecological Informatics (2022) Vol. 73, pp. 101838-101838
Open Access | Times Cited: 17
Prediction of Groundwater Level and its Correlation with Land Subsidence and Groundwater Quality in Cangzhou, North China Plain, Using Time-Series Long Short-Term Memory Neural Network and Hybrid Models
Mouigni Baraka Nafouanti, Junxia Li, Hamada Chakira, et al.
Natural Resources Research (2025)
Closed Access
Soil surface subsidence time series modeling of an area with Aridisols and Vertisols complex using surveying and drone imagery in Central Iran
Peyman Amin, Mohammad Akhavan Ghalibaf, M. Reza Hosseini
Dokuchaev Soil Bulletin (2025), Iss. 122, pp. 62-88
Open Access
Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping
Alireza Arabameri, Saro Lee, Fatemeh Rezaie, et al.
Frontiers in Earth Science (2021) Vol. 9
Open Access | Times Cited: 22
Understanding the Spatial Variability of the Relationship between InSAR-Derived Deformation and Groundwater Level Using Machine Learning
Guobin Fu, Wolfgang Schmid, Pascal Castellazzi
Geosciences (2023) Vol. 13, Iss. 5, pp. 133-133
Open Access | Times Cited: 9
Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam Using Boosting Models
Tran Van Anh, Maria Antonia Brovelli, Khien Trung Ha, et al.
(2024)
Open Access | Times Cited: 3
Analysis of the Contribution Rate of the Influencing Factors to Land Subsidence in the Eastern Beijing Plain, China Based on Extremely Randomized Trees (ERT) Method
Fengkai Li, Huili Gong, Beibei Chen, et al.
Remote Sensing (2020) Vol. 12, Iss. 18, pp. 2963-2963
Open Access | Times Cited: 21
Investigation of land-subsidence phenomenon and aquifer vulnerability using machine models and GIS technique
Adel Ghasemi, Omid Bahmani, Samira Akhavan, et al.
Natural Hazards (2023) Vol. 118, Iss. 2, pp. 1645-1671
Closed Access | Times Cited: 7
Mechanism the land subsidence from multiple spatial scales and hydrogeological conditions – A case study in Beijing-Tianjin-Hebei, China.
Han Jiao, Huili Gong, Lin Guo, et al.
Journal of Hydrology Regional Studies (2023) Vol. 50, pp. 101531-101531
Open Access | Times Cited: 7
Scrutinization of land subsidence rate using a supportive predictive model: Incorporating radar interferometry and ensemble soft-computing
Bahram Choubin, Kourosh Shirani, Farzaneh Sajedi Hosseini, et al.
Journal of Environmental Management (2023) Vol. 345, pp. 118685-118685
Closed Access | Times Cited: 4
Susceptibility assessment of earth fissure related to groundwater extraction using machine learning methods combined with weights of evidence
Aihua Wei, Yuanyao Chen, Haijun Zhao, et al.
Natural Hazards (2023) Vol. 119, Iss. 3, pp. 2089-2111
Closed Access | Times Cited: 4
Development of a Land Subsidence Forecasting Model Using Small Baseline Subset—Differential Synthetic Aperture Radar Interferometry and Particle Swarm Optimization—Random Forest (Case Study: Tehran-Karaj-Shahriyar Aquifer, Iran)
Zahra Chatrsimab, Ali Asghar Alesheikh, Behzad Voosoghi, et al.
Doklady Earth Sciences (2020) Vol. 494, Iss. 1, pp. 718-725
Closed Access | Times Cited: 10
Land Subsidence Susceptibility Mapping in Ca Mau Province, Vietnam, Using Boosting Models
Tran Van Anh, Maria Antonia Brovelli, Khien Trung Ha, et al.
ISPRS International Journal of Geo-Information (2024) Vol. 13, Iss. 5, pp. 161-161
Open Access
Employing machine learning to document trends and seasonality of groundwater-induced subsidence
Sumriti Ranjan Patra, Hone‐Jay Chu, Tatas Tatas
Natural Hazards (2024)
Closed Access
Multi-Objective Evolutionary Simultaneous Feature Selection and Outlier Detection for Regression
Fernando Jiménez, Estrella Lucena-Sánchez, Gracia Sánchez, et al.
IEEE Access (2021) Vol. 9, pp. 135675-135688
Open Access | Times Cited: 3
Spatial Prediction of Groundwater Potentiality Mapping Using Machine Learning Algorithms
Sunil Saha, Amiya Gayen, Kaustuv Mukherjee, et al.
Research Square (Research Square) (2021)
Open Access | Times Cited: 1
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