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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:

Inclusive Multiple Model Using Hybrid Artificial Neural Networks for Predicting Evaporation
Mohammad Ehteram, Fatemeh Panahi, Ali Najah Ahmed, et al.
Frontiers in Environmental Science (2022) Vol. 9
Open Access | Times Cited: 43

Showing 1-25 of 43 citing articles:

Prediction model for rice husk ash concrete using AI approach: Boosting and bagging algorithms
Muhammad Nasir Amin, Bawar Iftikhar, Kaffayatullah Khan, et al.
Structures (2023) Vol. 50, pp. 745-757
Closed Access | Times Cited: 84
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: 27
Prediction of permeability coefficient of soil using hybrid artificial neural network models
Majid M. Kharnoob, Tarak Vora, A K Dasarathy, et al.
Modeling Earth Systems and Environment (2025) Vol. 11, Iss. 1
Closed Access | Times Cited: 1
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: 38
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: 30
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: 23
Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature
Abdulaziz Alaskar, Ghasan Alfalah, Fadi Althoey, et al.
Case Studies in Construction Materials (2023) Vol. 18, pp. e02199-e02199
Open Access | Times Cited: 22
Comparative analysis of various machine learning algorithms to predict strength properties of sustainable green concrete containing waste foundry sand
Muhammad Faisal Javed, Majid Khan, Muhammad Fawad, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 13
Sustainable predictive model of concrete utilizing waste ingredient: Individual alogrithms with optimized ensemble approaches
Wei Zheng, Athar Zaman, Furqan Farooq, et al.
Materials Today Communications (2023) Vol. 35, pp. 105901-105901
Closed Access | Times Cited: 20
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: 15
Predicting workability and mechanical properties of bentonite plastic concrete using hybrid ensemble learning
Amir Tavana Amlashi, Ali Reza Ghanizadeh, Shadi Firouzranjbar, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Convolutional Neural Network- ANN- E (Tanh): A New Deep Learning Model for Predicting Rainfall
Mahdie Afshari Nia, Fatemeh Panahi, Mohammad Ehteram
Water Resources Management (2023) Vol. 37, Iss. 4, pp. 1785-1810
Closed Access | Times Cited: 15
Machine learning approaches to predict the strength of graphene nanoplatelets concrete: Optimization and hyper tuning with graphical user interface
Turki S. Alahmari, Kiran Arif
Materials Today Communications (2024) Vol. 40, pp. 109946-109946
Closed Access | Times Cited: 4
A comparison of novel hybrid ensemble learners to predict the compressive strength of green engineering materials: a case of concrete composed of rice husk ash
Ali Reza Ghanizadeh, Amir Tavana Amlashi, Samer Dessouky, et al.
European Journal of Environmental and Civil engineering (2024), pp. 1-28
Closed Access | Times Cited: 3
Analysis of power-law natural conjugate heat transfer under the effect of magnetic field and heat absorption/production based on the first and second laws of thermodynamics for the entropy via lattice Boltzmann method
Chuan Sun, Yuelei Zhang, Somayeh Davoodabadi Farahani, et al.
Engineering Analysis with Boundary Elements (2022) Vol. 144, pp. 165-184
Closed Access | Times Cited: 14
Prediction of Groundwater Drought Based on Hydro-Meteorological Insights via Machine Learning Approaches
Veysi Kartal, Okan Mert Katipoğlu, Erkan Karakoyun, et al.
Physics and Chemistry of the Earth Parts A/B/C (2024) Vol. 136, pp. 103757-103757
Closed Access | Times Cited: 2
Predictive modeling for compressive strength of blended cement concrete using hybrid machine learning models
Asad Ullah Khan, Raheel Asghar, Najmul Hassan, et al.
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 8, Iss. 1
Closed Access | Times Cited: 2
Optimizing river flow rate predictions: integrating cognitive approaches and meteorological insights
Veysi Kartal, Erkan Karakoyun, Muhammed Ernur Akıner, et al.
Natural Hazards (2024)
Closed Access | Times Cited: 2
Application of inclusive multiple model for the prediction of saffron water footprint
Zahra Gerkani Nezhad Moshizi, Ommolbanin Bazrafshan, Hadi Ramezani Etedali, et al.
Agricultural Water Management (2023) Vol. 277, pp. 108125-108125
Open Access | Times Cited: 7
An inclusive multiple model for predicting total sediment transport rate in the presence of coastal vegetation cover based on optimized kernel extreme learning models
Hamed Jalil-Masir, Rohollah Fattahi, Elham Ghanbari-Adivi, et al.
Environmental Science and Pollution Research (2022) Vol. 29, Iss. 44, pp. 67180-67213
Closed Access | Times Cited: 10
Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast’s Tumor
Hamzeh Ghorbani, Sahar Lajmorak, Simin Ghorbani, et al.
(2023), pp. 000147-000154
Open Access | Times Cited: 4
MD analysis of heat transfer of carbon nanotube flow on nanopumping process to improve the hydrodynamic and thermal performances
Guiyue Kou, Milad Jahanbakhsh, Mohammadali Molaee, et al.
Engineering Analysis with Boundary Elements (2022) Vol. 144, pp. 507-517
Closed Access | Times Cited: 6
Uncertainty and spatial analysis in wheat yield prediction based on robust inclusive multiple models
Fatemeh Soroush, Mohammad Ehteram, Akram Seifi
Environmental Science and Pollution Research (2022) Vol. 30, Iss. 8, pp. 20887-20906
Closed Access | Times Cited: 6
Hybrid and Integrative Evolutionary Machine Learning in Hydrology: A Systematic Review and Meta-analysis
Amin Mahdavi‐Meymand, Wojciech Sulisz, Mohammad Zounemat‐Kermani
Archives of Computational Methods in Engineering (2023) Vol. 31, Iss. 3, pp. 1297-1340
Closed Access | Times Cited: 3
Investigating the predictive contribution of attitude towards life and belief system on self-resilience and psychological toughness of cancer patients about the mediating role of emotion regulation
Hamzeh Ghorbani, Sepideh Asadi, Simin Ghorbani, et al.
(2023), pp. 000139-000146
Open Access | Times Cited: 2
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