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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:

Land subsidence hazard modeling: Machine learning to identify predictors and the role of human activities
Omid Rahmati, Ali Golkarian, Trent Biggs, et al.
Journal of Environmental Management (2019) Vol. 236, pp. 466-480
Closed Access | Times Cited: 130

Showing 1-25 of 130 citing articles:

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?
Yacine Achour, Hamid Reza Pourghasemi
Geoscience Frontiers (2019) Vol. 11, Iss. 3, pp. 871-883
Open Access | Times Cited: 252
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
Multifaceted anomaly detection framework for leachate monitoring in landfills
Rong Liu, Shiyu Jiang, Jian Ou, et al.
Journal of Environmental Management (2024) Vol. 368, pp. 122130-122130
Closed Access | Times Cited: 23
Land subsidence modelling using tree-based machine learning algorithms
Omid Rahmati, Fatemeh Falah, Seyed Amir Naghibi, et al.
The Science of The Total Environment (2019) Vol. 672, pp. 239-252
Closed Access | Times Cited: 141
Earth fissure hazard prediction using machine learning models
Bahram Choubin, Amir Mosavi, Esmail Heydari Alamdarloo, et al.
Environmental Research (2019) Vol. 179, pp. 108770-108770
Closed Access | Times Cited: 133
The effect of sample size on different machine learning models for groundwater potential mapping in mountain bedrock aquifers
Davoud Davoudi Moghaddam, Omid Rahmati, Mahdi Panahi, et al.
CATENA (2019) Vol. 187, pp. 104421-104421
Open Access | Times Cited: 119
Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management?
Hamid Reza Pourghasemi, Amiya Gayen, Mohsen Edalat, et al.
Geoscience Frontiers (2019) Vol. 11, Iss. 4, pp. 1203-1217
Open Access | Times Cited: 106
A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility
Alireza Arabameri, Sunil Saha, Jagabandhu Roy, et al.
The Science of The Total Environment (2020) Vol. 726, pp. 138595-138595
Closed Access | Times Cited: 91
Flood susceptibility modeling based on new hybrid intelligence model: Optimization of XGboost model using GA metaheuristic algorithm
Nguyễn Thị Thùy Linh, Manish Pandey, Saeid Janizadeh, et al.
Advances in Space Research (2022) Vol. 69, Iss. 9, pp. 3301-3318
Closed Access | Times Cited: 56
Application of the MaxEnt model in improving the accuracy of ecological red line identification: A case study of Zhanjiang, China
Zhijian Li, Yiming Liu, Hui Zeng
Ecological Indicators (2022) Vol. 137, pp. 108767-108767
Open Access | Times Cited: 45
A Comparative Analysis of Certainty Factor-Based Machine Learning Methods for Collapse and Landslide Susceptibility Mapping in Wenchuan County, China
Xinyue Yuan, Chao Liu, Ruihua Nie, et al.
Remote Sensing (2022) Vol. 14, Iss. 14, pp. 3259-3259
Open Access | Times Cited: 42
Water management dilemma in the agricultural sector of Iran: A review focusing on water governance
Milad Nouri, Mehdi Homaee, L. S. Pereira, et al.
Agricultural Water Management (2023) Vol. 288, pp. 108480-108480
Open Access | Times Cited: 42
Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022
Dharen Kumar Pandey, Ahmed Imran Hunjra, Ratikant Bhaskar, et al.
Resources Policy (2023) Vol. 86, pp. 104250-104250
Closed Access | Times Cited: 30
Monitoring Land Subsidence along the Subways in Shanghai on the Basis of Time-Series InSAR
Jinhua Zhang, Chang‐Qing Ke, Xiaoyi Shen, et al.
Remote Sensing (2023) Vol. 15, Iss. 4, pp. 908-908
Open Access | Times Cited: 24
Can river flow prevent land subsidence in urban areas?
Alireza Sharifi, Behshid Khodaei, Amirhossein Ahrari, et al.
The Science of The Total Environment (2024) Vol. 917, pp. 170557-170557
Open Access | Times Cited: 12
Risk assessment of land subsidence in Shanghai municipality based on AHP and EWM
Y. H. Zhan, Yichen Zhang, Jiquan Zhang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 1
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
Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions
Omid Rahmati, Omid Ghorbanzadeh, Teimur Teimurian, et al.
Remote Sensing (2019) Vol. 11, Iss. 24, pp. 2995-2995
Open Access | Times Cited: 65
Land Subsidence Response to Different Land Use Types and Water Resource Utilization in Beijing-Tianjin-Hebei, China
Chaofan Zhou, Huili Gong, Beibei Chen, et al.
Remote Sensing (2020) Vol. 12, Iss. 3, pp. 457-457
Open Access | Times Cited: 65
Sedimentological characteristics and application of machine learning techniques for landslide susceptibility modelling along the highway corridor Nahan to Rajgarh (Himachal Pradesh), India
Vijendra Kumar Pandey, Kaushal Kumar Sharma, Hamid Reza Pourghasemi, et al.
CATENA (2019) Vol. 182, pp. 104150-104150
Closed Access | Times Cited: 58
Land Subsidence Susceptibility Mapping Using Persistent Scatterer SAR Interferometry Technique and Optimized Hybrid Machine Learning Algorithms
Babak Ranjgar, Seyed Vahid Razavi-Termeh, Fatemeh Foroughnia, et al.
Remote Sensing (2021) Vol. 13, Iss. 7, pp. 1326-1326
Open Access | Times Cited: 55
Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility
Alireza Arabameri, Omid Asadi Nalivan, Sunil Saha, et al.
Remote Sensing (2020) Vol. 12, Iss. 11, pp. 1890-1890
Open Access | Times Cited: 51
Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation
Alireza Arabameri, Subodh Chandra Pal, Fatemeh Rezaie, et al.
Journal of Environmental Management (2021) Vol. 284, pp. 112067-112067
Closed Access | Times Cited: 48
Research on Mining Maximum Subsidence Prediction Based on Genetic Algorithm Combined with XGBoost Model
Zhongyuan Gu, Miaocong Cao, Chunguang Wang, et al.
Sustainability (2022) Vol. 14, Iss. 16, pp. 10421-10421
Open Access | Times Cited: 38
Global Dynamic Rainfall-Induced Landslide Susceptibility Mapping Using Machine Learning
Bohao Li, Kai Liu, Ming Wang, et al.
Remote Sensing (2022) Vol. 14, Iss. 22, pp. 5795-5795
Open Access | Times Cited: 32
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