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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:

Hybrid Machine Learning Models for Soil Saturated Conductivity Prediction
Francesco Granata, Fabio Di Nunno, Giuseppe Modoni
Water (2022) Vol. 14, Iss. 11, pp. 1729-1729
Open Access | Times Cited: 20

Showing 20 citing articles:

Assessment of hydraulic conductivity of compacted clayey soil using artificial neural network: an investigation on structural and database multicollinearity
Jitendra Khatti, Kamaldeep Singh Grover
Earth Science Informatics (2024)
Closed Access | Times Cited: 16
Water Quality Inversion of a Typical Rural Small River in Southeastern China Based on UAV Multispectral Imagery: A Comparison of Multiple Machine Learning Algorithms
Yujie Chen, Ke Yao, Beibei Zhu, et al.
Water (2024) Vol. 16, Iss. 4, pp. 553-553
Open Access | Times Cited: 11
A Stacked Machine Learning Algorithm for Multi-Step Ahead Prediction of Soil Moisture
Francesco Granata, Fabio Di Nunno, Mohammad Najafzadeh, et al.
Hydrology (2022) Vol. 10, Iss. 1, pp. 1-1
Open Access | Times Cited: 24
A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques
Ghazi Al-Rawas, Mohammad Reza Nikoo, Malik Al-Wardy, et al.
Water (2024) Vol. 16, Iss. 14, pp. 2069-2069
Open Access | Times Cited: 5
Predicting saturated hydraulic conductivity from particle size distributions using machine learning
Valerie de Rijk, Jelle Buma, Hans Veldkamp, et al.
Stochastic Environmental Research and Risk Assessment (2025)
Open Access
Predicting and mapping soil saturated hydraulic conductivity in the Beni Moussa irrigated perimeter (Tadla Plain, Morocco) using Random Forest machine learning model
Atika Mouaddine, Ahmed Barakat, Soufiane Hajaj, et al.
Modeling Earth Systems and Environment (2025) Vol. 11, Iss. 2
Closed Access
Predictive Modeling of Saturated Hydraulic Conductivity using Machine Learning Techniques
Moussa S. Elbisy
Engineering Technology & Applied Science Research (2025) Vol. 15, Iss. 2, pp. 21348-21355
Open Access
Ensemble Learning for Blending Gridded Satellite and Gauge-Measured Precipitation Data
Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, et al.
Remote Sensing (2023) Vol. 15, Iss. 20, pp. 4912-4912
Open Access | Times Cited: 7
Comparing machine learning approaches for estimating soil saturated hydraulic conductivity
Ali Akbar Moosavi, Mohammad Amin Nematollahi, Mohammad Omidifard
PLoS ONE (2024) Vol. 19, Iss. 11, pp. e0310622-e0310622
Open Access | Times Cited: 2
Dominant factors determining the hydraulic conductivity of sedimentary aquitards: A random forest approach
Martijn D. van Leer, Willem Jan Zaadnoordijk, Alraune Zech, et al.
Journal of Hydrology (2023) Vol. 627, pp. 130468-130468
Open Access | Times Cited: 5
Comparative Analysis with Statistical and Machine Learning for Modeling Overall and High Salinity along the Scheldt Estuary
Boli Zhu, T. Wang, Joke De Meester, et al.
Water (2024) Vol. 16, Iss. 15, pp. 2150-2150
Open Access | Times Cited: 1
Inclusion of fractal dimension in machine learning models improves the prediction accuracy of hydraulic conductivity
Abhradip Sarkar, Pragati Pramanik Maity, Mrinmoy Ray, et al.
Stochastic Environmental Research and Risk Assessment (2024) Vol. 38, Iss. 10, pp. 4043-4067
Closed Access | Times Cited: 1
Estimation of Reference Evapotranspiration in Semi-Arid Region with Limited Climatic Inputs Using Metaheuristic Regression Methods
Saad Sh. Sammen, Özgür Kişi, Ahmed Mohammed Sami Al‐Janabi, et al.
Water (2023) Vol. 15, Iss. 19, pp. 3449-3449
Open Access | Times Cited: 3
Hydropedological digital mapping: machine learning applied to spectral VIS-IR and radiometric data dimensionality reduction
Priscilla Azevedo dos Santos, Helena Saraiva Koenow Pinheiro, Waldir de Carvalho, et al.
Revista Brasileira de Ciência do Solo (2023) Vol. 47
Open Access | Times Cited: 2
Construction and evaluation of pedotransfer functions for saturated hydraulic conductivity in the granite red soil regions of southern China
Ling He, Xiaoqian Duan, Ding Shuwen, et al.
Journal of Hydrology Regional Studies (2023) Vol. 50, pp. 101539-101539
Open Access | Times Cited: 2
A Stacked Machine Learning Algorithm for Multi-Step Ahead Prediction of Soil Moisture
Francesco Granata, Fabio Di Nunno, Mohammad Najafzadeh, et al.
EarthArXiv (California Digital Library) (2022)
Open Access | Times Cited: 3
Predicting Saturated Hydraulic Conductivity from Particle Size Distributions Using Machine Learning
Valerie de Rijk, Jelle Buma, Hans Veldkamp, et al.
(2024)
Closed Access
Ensemble learning for blending gridded satellite and gauge-measured precipitation data
Georgia Papacharalampous, Hristos Tyralis, Nikolaos Doulamis, et al.
arXiv (Cornell University) (2023)
Open Access
Analysis of Water Volume Required to Reach Steady Flow in the Constant Head Well Permeameter Method
Aziz Amoozegar, Joshua L. Heitman
Hydrology (2023) Vol. 10, Iss. 11, pp. 214-214
Open Access
A Stacked Machine Learning Algorithm for Multi-Step Ahead Prediction of Soil Moisture
Francesco Granata, Fabio Di Nunno, Mohammad Najafzadeh, et al.
EarthArXiv (California Digital Library) (2022)
Open Access
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