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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:

Towards an Ensemble Machine Learning Model of Random Subspace Based Functional Tree Classifier for Snow Avalanche Susceptibility Mapping
Amirhosein Mosavi, Ataollah Shirzadi, Bahram Choubin, et al.
IEEE Access (2020) Vol. 8, pp. 145968-145983
Open Access | Times Cited: 65

Showing 1-25 of 65 citing articles:

Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms
Laleh Ghayour, Aminreza Neshat, Sina Paryani, et al.
Remote Sensing (2021) Vol. 13, Iss. 7, pp. 1349-1349
Open Access | Times Cited: 110
Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms
Majid Khan, Roz‐Ud‐Din Nassar, Waqar Anwar, et al.
Results in Engineering (2024) Vol. 21, pp. 101837-101837
Open Access | Times Cited: 28
River Water Salinity Prediction Using Hybrid Machine Learning Models
Assefa M. Melesse, Khabat Khosravi, John P. Tiefenbacher, et al.
Water (2020) Vol. 12, Iss. 10, pp. 2951-2951
Open Access | Times Cited: 95
Drought indicator analysis and forecasting using data driven models: case study in Jaisalmer, India
Ahmed Elbeltagi, Manish Kumar, Nand Lal Kushwaha, et al.
Stochastic Environmental Research and Risk Assessment (2022) Vol. 37, Iss. 1, pp. 113-131
Closed Access | Times Cited: 68
Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality
Abdulaziz Alqahtani, Muhammad Izhar Shah, Ali Aldrees, et al.
Sustainability (2022) Vol. 14, Iss. 3, pp. 1183-1183
Open Access | Times Cited: 56
Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods
Stéfano Frizzo Stefenon, Rafael Bruns, Andreza Sartori, et al.
IEEE Access (2022) Vol. 10, pp. 33980-33991
Open Access | Times Cited: 50
Prediction of sustainable concrete utilizing rice husk ash (RHA) as supplementary cementitious material (SCM): Optimization and hyper-tuning
Muhammad Nasir Amin, Kaffayatullah Khan, Abdullah Mohammad Abu Arab, et al.
Journal of Materials Research and Technology (2023) Vol. 25, pp. 1495-1536
Open Access | Times Cited: 39
Optimizing durability assessment: Machine learning models for depth of wear of environmentally-friendly concrete
Majid Khan, Roz‐Ud‐Din Nassar, Asad U. Khan, et al.
Results in Engineering (2023) Vol. 20, pp. 101625-101625
Open Access | Times Cited: 36
Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach
Muzaffer Can İban, Süleyman Sefa Bilgilioğlu
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 6, pp. 2243-2270
Closed Access | Times Cited: 34
Compressive strength prediction of concrete blended with carbon nanotubes using gene expression programming and random forest: hyper-tuning and optimization
Dawei Yang, Ping Xu, Athar Zaman, et al.
Journal of Materials Research and Technology (2023) Vol. 24, pp. 7198-7218
Open Access | Times Cited: 31
A comprehensive GEP and MEP analysis of a cement-based concrete containing metakaolin
Muhammad Iftikhar Faraz, Siyab Ul Arifeen, Muhammad Nasir Amin, et al.
Structures (2023) Vol. 53, pp. 937-948
Closed Access | Times Cited: 24
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: 13
Evaluating StackingC and ensemble models for enhanced lithological classification in geological mapping
Sasan Farhadi, Samuele Tatullo, Mina Boveiri Konari, et al.
Journal of Geochemical Exploration (2024) Vol. 260, pp. 107441-107441
Open Access | Times Cited: 13
Mass wasting susceptibility assessment of snow avalanches using machine learning models
Bahram Choubin, Moslem Borji, Farzaneh Sajedi Hosseini, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 55
A comparison of machine learning models for suspended sediment load classification
Nouar AlDahoul, Ali Najah Ahmed, Mohammed Falah Allawi, et al.
Engineering Applications of Computational Fluid Mechanics (2022) Vol. 16, Iss. 1, pp. 1211-1232
Open Access | Times Cited: 32
Mechanical behaviour of E-waste aggregate concrete using a novel machine learning algorithm: Multi expression programming (MEP)
Sultan Shah, Moustafa Houda, Sangeen Khan, et al.
Journal of Materials Research and Technology (2023) Vol. 25, pp. 5720-5740
Open Access | Times Cited: 17
Development of Prediction Models for Shear Strength of Rockfill Material Using Machine Learning Techniques
Mahmood Ahmad, Paweł Kamiński, Piotr Olczak, et al.
Applied Sciences (2021) Vol. 11, Iss. 13, pp. 6167-6167
Open Access | Times Cited: 41
Application of machine learning methods for snow avalanche susceptibility mapping in the Parlung Tsangpo catchment, southeastern Qinghai-Tibet Plateau
Hong Wen, Xiyong Wu, Xin Liao, et al.
Cold Regions Science and Technology (2022) Vol. 198, pp. 103535-103535
Closed Access | Times Cited: 25
Snow avalanche susceptibility mapping from tree-based machine learning approaches in ungauged or poorly-gauged regions
Yang Liu, Xi Chen, Jinming Yang, et al.
CATENA (2023) Vol. 224, pp. 106997-106997
Closed Access | Times Cited: 16
Spatial Prediction of Landslide Susceptibility Using Logistic Regression (LR), Functional Trees (FTs), and Random Subspace Functional Trees (RSFTs) for Pengyang County, China
Hui Shang, Lixiang Su, Wei Chen, et al.
Remote Sensing (2023) Vol. 15, Iss. 20, pp. 4952-4952
Open Access | Times Cited: 16
Modeling of H2S solubility in ionic liquids: comparison of white-box machine learning, deep learning and ensemble learning approaches
Seyed-Pezhman Mousavi, Reza Nakhaei-Kohani, Saeid Atashrouz, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 15
Combining modelled snowpack stability with machine learning to predict avalanche activity
Léo Viallon-Galinier, Pascal Hagenmuller, Nicolas Eckert
˜The œcryosphere (2023) Vol. 17, Iss. 6, pp. 2245-2260
Open Access | Times Cited: 14
Spatial modeling of snow avalanche susceptibility using hybrid and ensemble machine learning techniques
Hüseyın Akay
CATENA (2021) Vol. 206, pp. 105524-105524
Closed Access | Times Cited: 32
Real-time monitoring of disc cutter wear in tunnel boring machines: A sound and vibration sensor-based approach with machine learning technique
Mohammad Amir Akhlaghi, Raheb Bagherpour, Seyed Hadi Hoseinie
Journal of Rock Mechanics and Geotechnical Engineering (2024)
Open Access | Times Cited: 4
A Hybrid Multi-gene Genetic Programming with Capuchin Search Algorithm for Modeling a Nonlinear Challenge Problem: Modeling Industrial Winding Process, Case Study
Malik Braik
Neural Processing Letters (2021) Vol. 53, Iss. 4, pp. 2873-2916
Closed Access | Times Cited: 27
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